{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-Depth: Decision Trees and Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
    "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n",
    "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
    "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
    "We will see examples of this in the following sections.\n",
    "We begin with the standard imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivating Random Forests: Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forests are an example of an *ensemble learner* built on decision trees.\n",
    "For this reason we'll start by discussing decision trees themselves.\n",
    "\n",
    "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
    "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "![](figures/05.08-decision-tree.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n",
    "The trick, of course, comes in deciding which questions to ask at each step.\n",
    "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n",
    "Let's now look at an example of this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a decision tree\n",
    "\n",
    "Consider the following two-dimensional data, which has one of four class labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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jQJ0sHpMKry8RiF9j3b6ayX4jI4z27sYpLJQoVzfSe/Si+xczSrprerOzs+di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\nx7fzqr/V11bxzvBMfMrHmrU3CuX0fOsIc568/7Ib7hU2icuOcdckLm3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s7KmtVrN6RSaL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VODe/cl6vOFZHTqVO/arvsPMKdny9T63bgnnb+fOkb+8GEoBgNeWzdwt0bP45NubYnVO4HsI25l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11616a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4,\n",
    "                  random_state=0, cluster_std=1.0)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n",
    "This figure presents a visualization of the first four levels of a decision tree classifier for this data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.08-decision-tree-levels.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n",
    "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier().fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a quick utility function to help us visualize the output of the classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
    "    ax = ax or plt.gca()\n",
    "    \n",
    "    # Plot the training points\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
    "               clim=(y.min(), y.max()), zorder=3)\n",
    "    ax.axis('tight')\n",
    "    ax.axis('off')\n",
    "    xlim = ax.get_xlim()\n",
    "    ylim = ax.get_ylim()\n",
    "    \n",
    "    # fit the estimator\n",
    "    model.fit(X, y)\n",
    "    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
    "                         np.linspace(*ylim, num=200))\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "\n",
    "    # Create a color plot with the results\n",
    "    n_classes = len(np.unique(y))\n",
    "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
    "                           levels=np.arange(n_classes + 1) - 0.5,\n",
    "                           cmap=cmap, clim=(y.min(), y.max()),\n",
    "                           zorder=1)\n",
    "\n",
    "    ax.set(xlim=xlim, ylim=ylim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can examine what the decision tree classification looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LE2ruPffcCJgBWhnkrVxPcJcgHBooUWksMpmMmf/+C5u+W4FZajoqdzfCn1lg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b21e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_classifier(DecisionTreeClassifier(), X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QuXrzxPNTKC4s4cDxg0yf2Lx2cu9RDVGxU1tt4/j+bayYW9K0PMnDTUGgVwEK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\nVZJV3OI2c0lWcafb2XdgH4YxPlZXmtW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WaMBPi2ZoKHVerjdHo2LAIJlocFhb/IHOhXjd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118894860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helpers_05_08 is found in the online appendix\n",
    "import helpers_05_08\n",
    "helpers_05_08.plot_tree_interactive(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
    "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n",
    "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision trees and over-fitting\n",
    "\n",
    "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
    "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.08-decision-tree-overfitting.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
    "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xItv2fMKidZvve6+C1mtoXHQ2jI3GNq5+cKPGTeBU+m6mT2wZYr9VaMPbP6Jb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c4ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helpers_05_08 is found in the online appendix\n",
    "import helpers_05_08\n",
    "helpers_05_08.randomized_tree_interactive(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembles of Estimators: Random Forests\n",
    "\n",
    "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n",
    "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n",
    "An ensemble of randomized decision trees is known as a *random forest*.\n",
    "\n",
    "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bZE4OqCl77rkVMAG0Mshes5nAoADsGihR2VZkMhlz//1Xtn33MyZJKahcXRj9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TCxqZEP33z1ucUmLp6/QP6FSwAkTnmJaTOm4+HtzboJ4Ww/fIg6X198GhvJDBnHuFDrNpZeo0YR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\nVwghhLCz/w+BfjfvUVnKbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c0a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n",
    "                        random_state=1)\n",
    "\n",
    "bag.fit(X, y)\n",
    "visualize_classifier(bag, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n",
    "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n",
    "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n",
    "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
    "\n",
    "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
    "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bZE4OqKh477kVMAE0MshZu5mg4EDsmihRaSgymYx5//4r2775EZPEFJSuLox5\nZqlel9cIgvD4EMG3HZlGXqdmPSwLoCTiLFEjh9GrZ/d2mYYO/+QrJq/ZSNVkZ2LkdY4ZGZFxLZp5\n4bur+5N56zYHrS2ZvGR+g9fy9vLE+9mHhRHc3Vy5+crP2bF2I94pqSR5eeK4agkuNSpC1SRl5dRZ\nRuSkVpMuk+FaI9XgamAX5laurR27aA4bjkRgHR3LFCrX8kognT7Phn9+wtJ//LFFfx76ZGNtxYLf\n6G9a+MGDNCI+/xazu4moXJzpunQ+fTpQEpogCA0TGyu0I61p3XeU2vjbWD37Cuuef5W0tHSd31Nx\n5jw13zL6livJO3wC08uRtR4EnDVaSk9faPH1R8+eyrT13+KyfS0zwr5h5IyGC/i7DBlA4qPbL3p7\ncnTRHI7b23Eb2BLYhS6v/rx6Kt7KwoI5X35IurdndRENqKinbB4VTWZuHpoa09JPKkmSOPzOeyza\ne5hZcbeYf/IMWX9+n/v3638FIAhCxyKCbzsyHTeatBrvd28D3kCARsOKq1Gc/PRr3d9UU3fXFplW\ni1TPe2bJqHXvno2NjfBydcHIqPGJk6Ejh3FlxWKOODlyFwgP8MPztV+w9Hev0mvrj5SHfcvcdV/T\nb9jgWudZW1rgHFh367m8rBxuz1rGruU/59SeQ63qe2dx+VIkY6Ju1Gobl5HFhe27DdQjQRBaQkw7\nt6NpTy/lsLUl506eJTXyOr0KixhT4/Oau+foinJgX5T3kqqne9OBWzfvoDI2YjxQ9cb0jqkJThPH\ntujaKWkZaLQafD3cm33O3Fd+Rt7TS0i5n8aMLv4YV46EHe1scbR7tKTEQ94zp3DtwlX6FBYCkAY4\nKJUMUyrh5m2OfvwFaQP74ubq3KLvobNoaIXg45JRLwhPOsW77777rl7ulJaol9t0NAE9ggieOoHk\nqBtMqbF0ByA6qCvdp0/S6f26DR3I9qJibpYrOVVWRoFKzdMlpYwoKuYbc1PuBnXjdrcAVM8sY1Qj\nU8Y1FRYVs/HNdzH/+AvKN4Rz+Mo1PIcMwKKZ1ZXMTE1xcXJEoWj+RIunrzf3g7pwXitxTCZDmZPL\nNKjeIs6vtIxjTg4EPaEbX3h4uLHr3CV6p2dUtx1zdqLXb1/Fxsa6kTMFQdAbd78GPxLBV0+U9nbE\nnb+Mb0kpMuCUgz1OLz+Ph46LJCgUCkJCh6ANCabL1l0M02qRURG0hqg13Jk6nrl/fRu/oObv97rj\ng89Ysv+1SqSVAAAgAElEQVQwnmo17hoNvVNS2Z2dS8923hfVw9uL7uNHU2pvS5/DJ2ptEZcPZM2Y\nTEA909NPAplMhtOgfuzLzeOOXMGN7oF4/upnBPYIqj6moKiYvMIirCxaX4FMEIQ2aCT4imlnPekz\nZACJ33zMjp37kLQSfWZOJiDAr9FzCouK2fPv/2J+7QZaMzOMJ45h+rPLmzW1KJPL0NZ3WCumJc3i\n79RKDpAB5jdvt/g6rTVi3Ch+HNCHVZcikVOxo9FHHu4MyM4mr6AQOx2M9LRaLTs+/w4iziJTqSjr\n34c5v30FM1PTpk82EA9PdxbVk/2t0WjY/P7HOJ08g2VJCUd7hTDy7dfw8vYyQC8FQaiPCL565Ovr\nje8rLzT7+N3/+IjF+49UB770OwkctrNlYjPq+XYP6saavr0JvHileqr2pJMDfWZNbXG/1XZ119aq\nG3lfq2tyuZwFH73H9h/WkRMdhyLuNn9IfYD846/Yu3kXXf76FiH92ra5/Z7VYYz7YV31xgaqxGS2\nyWUsfuf1tn8Derbnpw3M3LareqZg2PlLbPj35yz59H2D9ksQhIdEtnMHpVarsb56vdZfkKtGQ/GZ\n5i0PkslkTHvvHTbPmsKO7oFsHTkM67+8hb+/b4v74rtwNucd7Kq/vmZtheu8thX0lySJXd+uIXzV\nS2xf8XO2fvp1o0uIrK0smffKC9jb2fJMQQEmVDw5zrqfStzqsDb1BUB74UqtHYWMAbMrUW2+riFI\nUTE8uo+PbWw85UqlQfojCEJdYuTbQcnlcjSPrpEFpHraGuLk5Miid99qc18GjBhK/H//xfbdB0Cj\nocuU8YS2MdFp708bGP7l9zhqK7J2i2Pi2SFpmffqLxo9zzj1Qd22+3XbWkprUvfPVWtsXM+RHZ+m\nnnrS5dZWGDexNEwQBP0RI98OSi6Xoxw1jOIabTGWFnhMmWCQ/gR1D2TOm68w563X6KWDDGPV6fPV\ngRfAEpCfu9Tkecp63lsqfZq3ZV9jHCaO406Noig5chnyMaFtvq4hdF84m5POTtVf3zcywmjaxBbt\nPCUIQvsSj8Id2Lxfv8RuG1u4eg2tmRnuMyYztAMGhAepDzizZiPGmdlouvgz9dnlTSYqSfUlfjUj\nOPjOmcZnR07ytFKJAtgkl2HUROJac4yeOZmTMrh++ASoVJgOG8Ss5QvbfF1D6N6rB0afvE94+G5k\nJaXYDx/MjKmGeWgTBKF+Ivh2YAqFgtkvPGXobjQqv6CQiFffZlFlwRDV0QjW37rDqo/ea/Q889Gh\npF+JwrXyPW8BQOiQJu+XdPQkLyiVnAQ0wGKtxP5T59C+9FybR3ajZkyGZq597ui6BXej2+9/behu\nCILQABF8hTY5sWUn82pU6jIGBp+5SExMPD1qrDl91OSl8zmg1VJ2/DRoNciHDmLW8yubvJ9xRjYm\nQM1xnG1aJsWlZVhbNq/ox6MkSeJQ+B5Ko26gtrVhxNKFuLo9mZWz9CmmvH3X/nvJTbExdqv3swJV\nGina8na9vyD0aOQzEXyFNtEWFtX5IXJRKonJzAIaDr4ymYwpKxbBikUtup86wBf1sYha98wO8G1T\nIYnN//4vEzaG4yBJSED4qfOM+N8HuLiIANxeClRpRLn0o0Tp0273GJq3Ay9VWp0AXBV42/v+giCC\nr9BuAseP5tqWHfQpLqluOxbgx6zhgxs5q/WmPreStfG3GXHhMs5KFYf9fQl88ZlW1zTOyS/A5cAx\nHCprJcuAuQmJhIdtbTLzWmi74LL2KYUZZ1ZITq/+eEVH1/t5Tq/+lCR7t9v9BaEpIvgKbdKjZzBH\nXnmB8C07sc/IJDPAj+CXnsOknZbpmJuZ8vSn/0fUtWiuZGQyY/RwTE3qbt3YXFk5ubjm59VqkwGK\ngsI29vTx0tQUcM0p3JZM2Vad9+g5WblQYqP/UWeBKo3I7HJS0+vfmEIQ9EUEX6HNxi+ag2b+TIpK\nSrGxstTLzjq9+4To5Dpdfb3ZEhxI95j46rY0hRyb/n10cv2OrrlTsF0TdtDXsWKv4Mjscm77z27y\n2hYmSZBxFa/67mHXfqPeKinpEg7acrxUFf1O0ZaTlQu3/WcTXCBGvIJhieAr6IRCocC2nuIOHZ1c\nLifk9ZfZ9J//ERx3i3QHewqnjmfutImG7lq7iylPJCsXUoP64VPQ+MNMUlA/+mbFABXHNyd4xeFD\nTi8ZXtHRep/mDS6zJg4folygZp2yEjsfMdUsdAgi+ApPvJB+venx4xfcS03D38621VnT7aVqdNpY\n9m59mppKjnLpVxGMmjkKjMyunDZ2avy4mqpGnykGmOYNLrOGMt3MkAiCrsmkhnbl1rWrJ/RyG0Ho\nTGqOTntnXG1WAG5JNm9LRoFxZoV6OUcQOovhfRuuPSBGvo8JjUbDyUPHKc7NZ8T0iTrZRk/QDaVK\nhUqtxtJcd/vm1gqgdj6gpHoKtznaY5q3NdcSQVcQ6ieC72MgJyeXnW/8kTmRN7ACDq7diPNvf8Wg\n0R2v1OSTRJIkPv04kptHPdCWWGHf8zov/94TTzeHNl+7KvD6FIRw82oMx9fc4GyGDBeXVJb/HHoE\nNn/6uaSkiMKCXFxcvfSSDCcIQtNE8H0MHPt+Hasib1TvyzvtQTpbf1jPwFHDxS/TGtLSM7l39x59\n+vXG3Kzx2tK6sG5DFGlrV+Ei2Vc0RMDnsk/5x8dtC74Fldm5JUofCgryOPmnO/inLKn4MBq+SPmO\nD9com1xiJUkS4R9tIe+ALUZ5Tqh7nGX0m30J6BHYpv4J+lFSUsT+z/ZTessUhb2afksCCeov3mF3\nFiL4PgaMk+/zaIi1TrlPuVLZ5AYGTwJJktj8wX/x2nuIrvmFHPb2xOEXzxDazpsJ3L1sgVlV4K2U\necOHK/m3MDNr/dpjqEiGQgnntkfgnTKj1me2t5bw9Z5/Mn5a13rPzcqF1HSJrI0XkK8bi5e2slJX\n5ACO/zsM/++7tfqhLSstnRPfR6BKM8XUR8n4n43Hxtau6ROFFtv0djjuJ1ZgjQKAc9eOYvVVIp5+\nLd+TW+h4RPB9DKg83ZGgVgAu9HRvU3GJzuTUkZOM2LQdD40WgJnJ99n15Q+Ujh3ZviNg84I6TeXW\nEped5qJo5t65KSalddq8lObVGchJchkSWqj8BQwgoeWu1QAs7cbUf1E7CC6w5sKV29hqa5fINI4J\nJCM9BVc372b1r6by8jK2/eYI/rErKvshsTF2Nc99s7LNm1rk5+dy48xlAnoG4u4tSj6mJt/D6Hwv\n5DX+3j0yxnEpfCuevxbBtzMQwfcxMPrZ5ay5EcvcG7FYAkdcnPF6aqmYcq6UG3m9OvBWGZJ8n8ir\nUQwbNqjJ8xOTUojcfxgjSyvGzJve7MSpafOcWXPuKA7Z4wAokWfhPk5DiNoe1E2fH2dWyJj4gzjV\nGDxHufSjhIfBJ3TOKDZs3o1P0tzqtgfBu3hm7HyMyhqvIqaw1tRpU9tlYWVd/4i5KWd2HsMzdl71\n1zJkOF+dweXjpxk0bmSrrglwfONhEr6T4ZI5gsPW17GYeY55byx8on++y8vKUKjr/hxKqif3z6Sz\nEcH3MeDk5MjS7/7L8X2HKMsvYOi0iTg7tj2pp7OQOTtRDtQc4962tcY/oOkRwqk9h+DDz5iVl48S\nCN+1n9H/+Tvu7g0nNGk0Gk4eOUFpYTEL/17OqX2xZBepKR/hw+I5cxs8rz5O9tDDtKKfBao0clxl\n3Ex++LmllQ1j3+/O+e83o0wzwdS7jGk/G4ZRM8p3DlsymL2nduGdNBOAUnkWdpPysbRsXQZyWaES\nY2oHBFPJlqLclpfivHLiHPdOplNOPiXH3PHJnQSAW+EQ8je5cm34Bfo2Y4vJzsqvaxDHeofBle7V\nbVnWkfSf1MWAvRJ0SQTfx4SxsRETZ001dDc6pPGL5rDh+CmWXLuBKfBAISdl6kSGu7o0ep4kSWSE\nbWFeXj5QEbyX3LzNth83MO+t1+o9JzMzi31v/pmZUdFYAPt8vJj2zm8w6u3IObtxyMp0PzIJ6BFI\nwActT5Jy8/Zi2mdwbuNW1PlynPtZMGru/Fb3Y8C0wewNO4pn1vjqtvte+1g2dWyLrnNkzQFyPgvE\nTjmcdA7gx5han9uq/Ui5HEnfJziZXyaTMe3d0Rz5ZANlN80wctAQON+JoL6tn2EQOhYRfIXHnoW5\nGQu/+IDDm3egycjEvm8vFo4f1eR55UolVmnpddqNHtRtq3LimzWsjIqufv8+JymFLd+uocen9Qdr\nQ3Pz9mLOG146uZaLmzs937zHjTVbUadaYORTxODn/bGwaH5ZUUmSSNxVgreyYgTnSCAZ3MCDAdXH\nlJGPnW/HqjJmCG7eniz/oGVbbgqPDxF8nwDFpaWkZmbj5+6GsXHn/Cu3MDdj+lOLW3SOqYkJBb4+\nkPNwVyMtoPZvOOHHNDmlTua5adJ9oGLzgaRm1El+nA2cNIwBEyXUahVGRsYtfi+r1WrR5D1MFLTH\nnxTOY4kLtnhTRgEZoVuYOn2lrrsuCB1K5/xNLFTbu3o9si078XuQzu4AP9xfWMXQiWMM3a0OQSaT\n0e2FVYT/38dMTUwmRyHnwIC+LHj+qQbPUdbzLljp4UYPU18KHNNwyrhKlAudOgDLZDKMjVuXaa9Q\nKDALKoLMh23BzCF58ueYunfB1secqTNXYtTMbHFBeFyJn/BO7HrkDXy/WUNIacVylu53Etj9yZcU\nhQ7GykJM6wH0GTKAwLBvOXHoGHZODjw9dFCjo7mhzy4nLCaOuTfvYAIcdnHGZ2XFiNvG2A0vVVqt\nXXSEusa/PowDZeswvtYDtXkhxiPu8dy7v2xWEtmTTJIknWSAp9xLIDUhmd5DB2JmLn4PGIoIvp3Y\n3VNnmV1aex3puNQ0Th8/zcQnYMu85jI3M2XyzCnNOtbTy4M5q7/g2K59KItKGD5rSqsyz5Nsouka\nfxXsmz62s/Hw8+Hpr71Je5CImZkH9g4jGjz28rGzJBxLR2YkETKjK8H9e+mxpx1DTlYW+/55iNJo\nK+RWGjynGDP52ektvo5Wq2XjX8PQHu6BdXEfrnsdpc+vHBk4cVg79Fpoigi+nZixkyOlUGtxSKKp\nCV6NvNMUmmZuZsrUhXNafX6STXTFDkWOLdsisDORyWS4e/g1esyJjUfI/I8/tuUVac9Xjl2i/C+X\n6DNqoB562HHs/tsh3E4uQ1aZbVCYcJ8zLscZPmNMi64Tsf0wljtmYF75xOeTMotrX26jzxhlq18j\nCK3XtrI0Qoc2bu50NvfuQVWphRLg4uhQuncPMmS3BGjx3rxPont7CrEtf1gQxDlvIDHbkxs5o/Mp\nKsxHc829OvACWKs9uX86v8XXyokpqw68Vazu9uXe7fg291NoOTHy7cTMTE2Z899/sWvdZuTpGSi6\ndmHZ4taP2IS2S7KJxiP+KjiKmtxNURfUHRtoCh6WW7wTHc/VzdGoC4yw7y1jwsppKBSKOuc8zoyN\nTZBMy+u0y0219RzdOFMXCQ1qFDV+7Zc438HNa0AjZwntRQTfTs7G2oo5v3jG0N0QENPNLWXRowQp\nUaoe9alRYt1TBUDSrTucev0BHukLAFAeK2ZrymYW/WGJwfrbHkzNzLEelYtySwkmVCRHZdhfYMCs\nlpcIHb1sHOtPrcPz+mKMMSPP5DbOs4uwthYbYxiCCL6CoAdxZoUEusrwyuocgVelUnL+0EkURnIG\njRvVLkuDpr0xie0l65Cu+CAZqTAb/oD5L1VU6Lq8NQqP9IfVukywJOO4M0Wv5mNlbavzvhjSvLcW\ncsBlN1nX5Mit1PSd49+qxDNLK2tWfjWfk1sPUpqpIWiIB72Hz26HHgvNIYKvIOhBcJk1SekS2uxy\n+jqmPdYBOOnWXQ788SLu8TOR0PBDj01Mf38kHj4t3ympMbYODqz6eBn5edkoFEa1gqqmtO6UtKLE\nmtLS4g4bfO9Ex3Phhxso00ww8y5n5M8H4+HXdPKjQqFg2s90EyTNzC2YtGJG0wcK7U4kXAmCDhSo\n0ihQpZGirft+ropPQQi3/WcTmV1OTHkiBao0PfZQd05/dRW/+KWYYoUZtvjFrCDiq/Ptdj9bO8c6\nAdVzqB2FRvdrtWl63sHZxaPd+tEWhQX5HP99PE5HFuIRPRuH/YvY8/Zp1CqVobsmGIgY+Qo61VhA\nac5or+r8x2VkWBVwc3r1ByAlXaJE2fBoJrjMmjj/2aSaJEHGVbxUuh8Fl5YWs/vD3ZTcsEBupcF/\nmi0j5rVs84PGlCXWXZZSlqjfBLJhU0ezL3kXSfsuQIEFxt1zmPxmw+uFDe3s9pN4JtcecbrFzuTC\n4ZMMnzq+gbOEzkwEX0FnqgKnqk/ddZjG1y5R0EigqXmu8bVL7ddJHaoKvOfsZkPlCpjgsqa36wsu\ns4ayEKJcgIyr9GjFvdUqFad3HqMorYyuof4E9X34DnDbX7bjdGAZdpUbsT+IvsVlu7MMGKebYgom\n7iq4XU+bnk19YSaa5zSoVUpMzZq3B7OhaLVSreVCADLkaLUtz1oWOgcx7Sy0WtVUa9X/VH0G1ht4\n4WFAfvScqv/VPOZxUjXiDS6zblbgramxEXJjystK+eHFdRT+fQQm387h6otG7Pt6F1Ax6i2/5Iqc\nh0tu7Mq6cfdIwzs1tdSAp7qR5LEbLRo0qEny3s7gpw1Ty1qhUHT4wAswbO5I7nvurdX2IHAXQyaN\nNlCPBEMTI98nSHu8Y2xJwGxLcK0aZT4JxSlSrsdxc899jCxlhC4aha2tPZIkEX1wE9pLx7mYaITX\n5f9gRMX0r2NZCPe3plK4NA9jIxOQS3UvqsNthoMH9sJ9nSdnw3chk8tYNHc01jbtn+R05dg57hxJ\nQyaDbpM96TNiULvfU1dsbe0J/asfl3/aijLVCFMfJZN/PkhUlnqCieD7hHicR5c1p3eH5u1ol/ek\n+hZnVoiFSVKd9lPfR5L2dndci+ahRcPmfduZ9p+BpG7/nPlrPsJTo6GYyRRR+5e2ZUYQ9+/dI7hX\nX8wHZ6LZ87CYQq5FLIGTdJuIZGvnwJRnZun0mo05FX6cB//2wq50OABxx6Ip/8NpBk8JbdX11CoV\nZ/YcpyirhH6T++Hu3f4lV4P6hxDU33C7XZWWFmNiYtYuhUgOrt5L6mElUpkcq35lzHp95mMxI2FI\negu+j2tmZ2fS0QOvRqMh8nIkNveTGBpUO7jm9OoPyRX/7xUdbaAe6kacWSFdE3bgZA89TH2Ji43n\n1uVruPby5Pa3fngXVVQckqPAN2E+R77+Du2ROG5r3sWEArScwJQCzLCpvmahTyT+3SoSd+b9cR67\nbbZSdN0UhZWGbjOd6Tuq4yYjNUfCnjzcSsdVf+1QHMKtndsY3Lz9MGopzM8n7NXteFxbgAlWHFp7\nhoBf3WXEvDG663AHcjfmJhGfXENz0wEci/Cba8645ZN0dv0TWw5T/NkAPDXuAGjuqNmu2sTidztX\nwRNd01vw7ei/+AXDunP7DqdPbKVPX2syHVV8uOcai0PHU2CuICsXUtOlWmtlnewT6WHq2+R1G3ro\n6ygj5zX/+if9dhxnZmkpUWYmyJgMLKt1TOK5NAaVh1e/xy0gjnSewkL+Zxy0PUl1PUz3522qRxqm\npmbM/+1CfX8r7UpTWHe0pi1q3Qju6I9H8L22Cnllyot7fig314czdKaq021rqNVqOfaPq/hEL61o\nyIPMz+K40fUKPYf018k9Uk8V41QZeAEUGFF4yUJnWyB2VmLaWegQLpzey6QpD6dG/fztWHPyNv5j\nllBi50NwQUUyk09BCHH+PtVLdXqY+tYKsDWDamNT7QWVGdUtDcI175WiLSclvZ73q02oWm50OXYv\nE7YfoWeZEoDeZUpelu9nNeexZwgASkqwKwuqlUBlQzDmWCB/di+mnvdZOC5UL+9cmyM7PYOTayJQ\nZZpg2VXDxFVTMTFp+zIki5AStDe11QFTgxqLnmWtupbygREWj+SaGqd6kpeXhZOzewNnPZ5ux9zA\nOmZIrTaHsmDunAjXWfCV1fMMJFO0/N/Fk0YEX6FDkMuLqLm5rVwux0xeTInSp04WcXCZNXH4AFeJ\nKU8kyqUfQEXd5BrBsbHZlqolTS15HZKiLa++V5X6+tccwWXW7D19i16VgbdKX62KVLsPMcn7nGIy\nuGm6Ayd5QJ3zE338+cVLb3WokUVxcSHbfnUM35tLkCFDfVBJWNw6Vn24qs3Xnv76dMKL1qG65I4k\n12I2JIP5v5rbqmtZ+GrrbDCg8knG3qFfI2c9nqzt7FCaZ0LJw58hCQm5ue6WOAVMciLx7C3sSrsB\nlQ+MocoO9bPZEYngK3QIEnVHRyqp8SnAqkBYtWQnp5cMV6PmJ3m05FVIrLq0uoBGa4JtfSwGDOWG\nmSk9yx5WxYo1NUFl243svFuYY8+I8re5rPwKFWUYYwZAjkUMQ16b0eF+uZ3efALvmwuq17MaYYLp\n6SHciY2lS/fubbq2paUVK/61nOLiQmQyGRYWVrU+z8nM4Nz2swAMnTMMB2eXBq81ftUk1kb9iMOF\nKVhoXEl1O0jvZ9063Y5IAO5ePkgjT6A+0Bujyn9jKT67mLVouM7uMXhyKBrlSRIOXEdbJsNugMSc\nF+bp7PqdlUySJL3MD2SX39HHbYTH1OkTxykvi6RHTyckSeJ0xANs+kyjxGw00tnbSJJEj/79awWc\nOLNCoHIkXJk97OX68PPuLQjEjYlVl3Iz2bv6Xrp0/r2XmLpzNcHKcuJMTAkLnUH+sX9hz8ORigYV\nN7p/gKt5dxTmWrrOcG51lm9DJEni+MaDPDhdhkwh4T/JkaHTRrboGrs+3YHZD7VHo6Xk4fbhVQaP\na7/1rPGXb3D6jyl4PqjIvrrvvp/Qv3kRNKBng+dIkkTkqXNkP8hm8JQR2Nh03p19VColB7/fS2Gc\nHGNHFUOWDcArwM/Q3XoiDO/bcCkNEXyFDuNm3E1iblxChoJhI8cRX1bGhmV3cY8ciww5eb1OMOsf\n43Fxr/+9XFUwBqqX8Xi5yuhuZM6d23e4cuEIMlkpElaMGDUVd8/mLb+pCr66DrxV7l6JIDfyDHZ9\nhmHj2ZV9C5NxL3pYjUqLFuVz4cz8ZeumWZtj/3e7Kf/fUCw0FSPGfNPbeLydzPBZzQ+at65Hc+kX\nWpxK+lS3Jfpv5akN01r93leSJI6GHeDBiYrZAfdRpoxbNrnWQ9j618JxPjG/1nmZo7ew7GMx+hIM\nq7HgK6adhQ4jMDiQwODA6q+/fPYIwZd/Vj2NaRv5FEc+3cDS9xfVe36t4FgWUhmMkyksLOL86c2M\nm+AFlXui7tvzEyuffaNdtsJrqYD+I6H/w1Gm8dhjKHf1qd6/NcV/O3OXtO9SobSjajw1D6dqbcu7\nknAgiuEtWMrbrVcIKb88RMLmBBTpLmgDUhjyYpc2JVwdWbOfok8H4qpxBaDwcjqH1PuYtGpa9TGq\n9Lp/h6r0zpW1LHQ+hv/NIwgNKIu1rVMPt/xu86aSHxaxkBFx9DAjR9ceLYeOdOT0yVOMHjdGN53V\noYV/WszRrvvJi5EwclAxc/lQHJycdXJtlUrJsbADFN6WMHHTMHbleKysbdCW131/LClb/k557NKJ\njFygorAwDzv7IW1+L516TIl7ZeAFsNS48uC4CmrkcJn5lUNc7fNM/VqXCS0I+iKCr9DhFBUVc27t\nNrSqeyiZWT0CBDDyy0XbLabJa1SMnyumnG9rNSgUtYOAsbEClUpZ36n1X887mUfzQ3WdgFXFyMiI\nSU+1z56r638fhvORpdhghhYtYefXsOqbRVj3L0NzR4WCihFjOQU4DGpd4DQyNsbeQTcPC2jqeShQ\n1/565ItD2JuyFucbFe98M3vuZ9qLLXtfLQj6JoKv0KHcvniNxOfeYO7NBGYDXymOkaTZgTV9yHLa\nz+ypufRuoMJVQ9nLw0aM4eSR7xg+8uE73tMRGSxctrxZfaqZuBWrLq3zeZxZIcFl1pSVlrDnkz0U\nRZuisNYQOMulVmLUrevRJMbcpe+YQTi56r/IR/y165hHhFZnTcuR4xG1kNPbjzHr9dlsV2+m4LIZ\nMoWE4wgNs5/XzzvT4qICTEzN6q1zbD9Yg/J6MSZYAqCkGIchtR+DPHy8eWb1Yq5GnAFgxsjFnTJz\nWehcRPAVOpQ7//ofy24mVH/9qiaJ//N/BuuxT7Nqtgf+3vUXBihQpWF87VK9AdjJ2ZGuQRM5cew0\nMlkJWq0Fg4fNxdS0Ze8iay43qlJz1Lv1L+E4HViGTWVBjITr1zG3uUzI0H6E/XkdRoeGYF8+iz1f\nn8Dr2WuMXz65RfdvKa1WS1ZmKrZ2TpiampGWeB9r5YRax5hgQUmWElNTMxb/qf3LAd6Lv82VLdfR\nFMsx9ikhL1KONtYVbIpxmaJm+kuzqqeqi4sKMLKQEdvnU6wLAjAyNsFhmJoZL86pc12FQsHAMa0b\n7cZfu0HMvtvIZNBzWiBde7Vmk0dBaBkRfIUOxfxucp22ILMCxv/cDdA2WhSjsXW7vfv1o3e/1hVR\neDTo1jfNXFSYj/KCR61KVA5FvYg/sI2i/EIs90zDUqqYivXIGUPyT4fJn5GLra19nWvpQtSpK1z6\nIgHjOwGoXKLwni9j+MIRbPjqID6pD6e0My0iGTymW7v04VH34m5x8tf3cU+ryEwuJpN8IujBOCiA\noh8ecNrnGCNmjiM1IYm9r1/CK2EOPVGQID9MfvB1xk6ZodNR7aWDZ7n5nhnOBRWj/PP7L1D454v0\nG/P47JgkPJ5E8BU6lNIAH4iOr9VWEtK9Q9UGr5pmrklCAqmed6QSZMYWVAfeKg4ZA4m/EsngsaN0\n0qcTKRGU3tuPk1EphUWWxH/Sky4plfV8UyDnq1iSet+lz6v2XP82HJO7XVF6JOGzUE7XHu07Aq9y\necuN6sALYIkzRpiiphwjTLHSuJN24Ry5oZns/DQc/4RXq8tJBmgnEhNTwKE/XeHpdT5NbsV3YtMR\nEj9X3QoAACAASURBVPcXoi2VY923nBmvzcTU1KzOcXFb03AteNgnl7zBxGzZKoKv0O5E8BWa5e6d\nu0RdPYcMOYOHj8Hdo33eWXb53Ytsuh7N7HupaIHtXbwJfuuldrlXc3U3MgfXUqBiVJ6SLlWWt3w4\nCra2tsNo4H20hx/WH861iCVwggf5mXkUUogpDwN2nv11QkOCdNK/K6oUjO+vYdY0b8CMc4fTKUip\nvWuNQ1l3bp8MZ+Zrs+k7Tk16WhJOTmP0uu2btrDurxsTrFFRihGmaNFyJzaG4nkO2OZPJ4YtONMD\nVyqKZcgxxv3WZM4fPMGI6RMbvM/ZvSfJ+qgb7uV+AGjiVGwv3VzvLjvqvLp9qq9NEHRN/JQJTTp/\n5jT5uRcYOswZSdJw7tQaevSaQVAbSwbWp+vA3nhcOcCef36MTC5n1O9ew8ys7YX526pm0lV3T4hV\nJ5OSLpFUYwA26INuRLy3DuMoa+TWGrrNcqTvqFGoVSp+OrEWh1OzsJScyTaLxn5+Jo4uratSVbWM\nqirTOunSXpaPf5hM5upjSqT5XSxKHavbNKgwsat4KDAyMsLTq2696Pbm1N+YwkM5mEsO1W153MOP\nitH/VZtP6XHnJcwra3w7EcgNNuFCCDJkaChHQo3CqPFp58SjOTiWj6n+WoExhResUKvVddZ1mweV\nIN2Uqpe0SUhYBNVNqhMEXRPBV2hSwp1LjBlXMW0qk8kYFurOqRMR7RJ8ASwszBn9l9+3y7V1pbuR\nOd09H221IPAHqzrVsIyMjXn641VcOBRBzr18+g8LILB33aShlqgoo5lMXLI3yGTUrFPnH2iHdswG\nVPt6Yow5EhJJQZtYvrB9li8115hFk9mespXkw1bIi6whJBm/HlpyU7ajsFbjnuWI+cna78Ad6Uom\nMWQSgzMhpIXsZur4+ousVKtvhZSs/u3tprw2kW3ZazC+HIIk06IZFMv8V2e24bsUhOYRwVdoklxW\nXrexvjahQXK5nKGT26e+se+g6Rw7+S6TxntXt3n8P3vnHVBVfub9z7mV3nsHUQFBRaVIR7HrzOio\n4+iMU5NNdpNsNluy2X13N9l9k32TTbJJdjfZTMpkmqOO41jHTlVsKBaKIAJKk97Lref9447gHRC4\ncBHU+/lLD+f8znMp5zm/p3yf9D4GZh+js0KK3F3DlldXYGtrfnnM1uZmMn+Tw0CVEoWHmpjXogiJ\nmDPiuYIgsPGvN6P6i376+3txcjbe+R/8+X5ERCNhlR77arr8C/ESFiALusmGP0sZU5UsZLkb9/Iq\ncFSFAqBFhX1c74iFWo4uLrzxP6/QUH8PQRBwc5tPZXkpGi9vHBydZ4QCmoWnE8tvloUx0emNJ8iI\noohetJ0maywARnOErW3tcV68nqNZuYhaEfqcCJv3bbxXTG0VsyiK7P/uMQILX8PpC4eZVXIIl/dc\ncXJxfeR1SivrEXPNidsTOXDuUwIqX0RAoFtah/8W2PDNH5hkV+zqJNQD2VR/fgP9gAT7hWo2fXP0\nnmVvnwCu5RRw5BuXaavsRSopQqFU4BSvYe13l+Pq+egpSRYsTATLYAULY3Kv+i7Zp/cwf6EdqgEd\nJcUqNm79Ck7OM2OA+0xiqocwjMQtq27m+BuKwR7cu6+vh/wDOWjVOuKfT8TJ+dHOcKJcP3+RO98I\nwF7vi5o+qshEjxartXf46g//akJrtrU0c+7jc2g7BXxinYlbaZ5q8LHQaNS8//JRNHcc8SQKOwyS\nliIi91N2sfOXLz8WOyw8XVgGK1iYFAFBgbzy5t9w7co1bB2UvP7ViBk3S9bCEDV3qjj+d1fwq3wB\nCVL27z1O3D97My9+4bBzRVHk+O+OcD9Th14lwW5hH8//3XNYWduMsLIxapUKqV6Jih5K2U8kLyFD\nScexKg567Of5vzRdIcvFzZ0N35xcPnwiVJQU43AnhgauDjpeAAEB7Q1Penu7pyRsb+HZ5dFu2YKF\nh5BIJCyKWUTk/HkWxzvDOf/HqwRVbkWGAglS/BvWcfW9yhHPzd5zEvVvE/Ar20RA9Qs4HtjKwR8f\nGtd9opMSaA4/SRWZRPHy4LB2JzGY9oOutLe1mO0zTTWevr70OVYjDlPwBqxVyGSWKUkWzIvF+Vqw\n8JShbhjemqWpH1mUoiF/ABv9kACIFBldV6wZTzZKJpOx8gex9PvcHhzI8AC79lAaau6ZaPn04eLm\nge2aOmxwpZaLg8dVdOGY2j2iQIcFC5PBEna2YOEpQxkwAIVfOhY4cnW6RDbcyUrkjDu6ETA7hMSv\nL6Dln1uwEd0Gj3cGXiE0/PEoZ5mLF/9uK+ejsik6cZmi+vO4efjgEaNk7c7N022ahacQi/O1YOEp\nI/UrCRyo+ADv4ueQoqA++Chpb488LEDw7eS65H2UekPxXCApuCZrRzz3USSsS2fvtd10nQjFoSeU\nFv+zzP+6OwrF9IujmIIgCCSsTSdhbfqY597Mv0LZ8XuIIoRm+BCdGjfq+b293QCWvLGFQSzVzhYs\nmJFSbT/5eSJ9WXeYHT2X4DDzSEiOxkjVzlqtlsunc1EPqIlfkzZi2PRa7mXK/o8NLt1RAKjooXz+\nf/Htd/8WicT0jFRDbQ31VfeIjFk0IdnK84fzuH2gBW2HFOuwftZ8JwMnV/NWaYuiSEHWOZpvt+O/\nwJvIuMUm1zBcOpFP1Q8dB79vHTZl+PxdPYnPD+/jHujv49N//Qz1RW8A5DENvPj9F7C2Hl+rXl11\nNVcPXwNBJG5THB4+PmNfZGHGYKl2tmDhMXC7rJx3/vk4ypOb8e1/novWRRSs/5gt//D421RkMhlL\nVy8b9Zzyz2tx6R4aKqDEDoe2iHHle0fC288fbz//sU8cgeJLhdz9sQvevQYHJlaKHOz8iNf+e/uE\n1hsJURR5/+//hMPptdjpvSmTV3Jr0x62/L1poxTLDzXi2Z00+H+nvrlUHi4m8fnh5x75xRHcjm8f\nnHalP6njqMM+Nv/jljHvcy2ngBv/NoB36yZERD4/eoqlP2xn7qJ5JtlrYWZiKbiyMONoamyhob5x\nus0wietXr3Ixdy/2WZvw609GQMC1PwrhwFJuXrwy3eaNiF49/M9f0EgR9SNU/I5Af38v546dprL0\n1qRtKTt5F9feqCE7EBCuzKG5qW7Saz/gSnb+oOMFcNSEoD00j+qycpPW0XcPV8rSdo38KO0tsjIa\nMylBSk/R+MLxRR/X4N1q6HMWEPC9v5LCXabZamHmYnG+FmYMPT29vP/7X3H18p8ouvYB7//hF7S1\ntk23WeOi/NYlBlqUuHcZi0I4akKouWY+B2JOfJJs6ZUOzUfWo8dqYQcy+dhtNQWnLrBrSxYd/5DI\nxTf1fPD376PVmpYrfhhBOny3LUo1SKXmC841l7cNOt4HuPbPp+Ja2SOuGBnbeSp0DH1WPXpsI0ce\nxiC10w07JrMf38uNpnn4Z9c0WYKVTwsW52thxvD5wT0sX+nEwmgvohZ4sWKVKyc/3zfdZo0TNeGx\n1rTZXjY62iWtQxLdxz2HYm5ZdU+TbSOTvHE5Vl+7RKn7nyiSf8g5+b9zO7+Sd7Z/yNHfHED/iB2w\nVqvl+jv3Cah7DgW2uA1E4nJiC9l7T0zYlsj1c2lyvjT4fz06hLg7uLh6jnKVafgt9KFDYVx70uhw\ngcjEaJPWWf+X62lds5tal9PUOp+hKeMj1n1n7Yjnzn7enTabksH/t9vcYtaG8eWxlSHGDl1ERDnL\nMnHpacHyGmVhXKhUKgRBQKEYfYj5ZBCELqRSt4f+LyARuqbsfubFkaDZArYvfkzHbnuc1OF0Se4i\n3XyIb+1cwy3dAA+mED1O6cnREAQBZz9HfLsW4KAJBEDUiNws3YW+dDmfc4j1XzeoTd1vuIdcrsDV\nzYuGuiqsK8KM1lJgS1f58F3eeJkdFUH/D65QtG8/2g4JdhFqtnxrIwDFF65x5Y93UN1ToPBVE/1G\nMPOTFpl8j6i4xZRt2kPzoW7c+hbQ6HAB1+2NePmNXqn8ZZRW1uz40XZ6e7oQRRE7+0dXR8etTUJp\nf5mKU/tBhDkrfFiYkjyu+6R9cymfN32Ic1EqekFDZ3QeL35jjUm2Wpi5WJyvhVHp6enl4L73sVJ2\nIoqg0bqy+eXXkY8jNGkqev3wNUWeDGWhdS9sZd+u3zH/eTV3w35EWb41qRuXsn77GgRBIFxmTan2\n0bsWvV5P7v5TtN5UIXPWkrQjGVd390eeby7u5bbhpBqq0hUQsMMbEGg9Dy0bGzn8/dPICiMQ5SpY\neooN31uN2rsQGobal/ToUHhO3PkCzE9ezPzkxUbHenu6uPB/awmo+6JAqREu1x8lcHc7jo7OI6wy\nOpu/+xLVG29TUXiIFYnRJjveh7G1cxjXeQuTY1iYHGPy+j4B/rz57jaKr1xFJpcRtuAVi7rcU4TF\n+VoYlSOf7SJ9uS0SiWG3plZpOfzZXjZt3WGW9TUaDaePfY5a3UFb6wCFV/qJXuwFQEV5G37+C8xy\nn6nGxsaanW9/i/sNTYTNU/OV7/iZdP0nP9qD9adrccAZEZH95/ay9Z3lODq7jH3xJBDkw3OtejRI\nvng0nPx5Nr4XXzGM+VOD7nQs2V778dliQ+vvinHtn4eGfuqi9vLyKyOU+06S80dy8a0z3u353V/N\nhYOHWbXzuQmtGTRnNkFzpnbik7mQSCRExSyZbjMsTAEW5/sMcL+hkXt37xG1IApra9Nk8gQ6kEiG\nilQUShk6bavZbPvw3V+TvtwBKys5Wq0b+z+ppKfHBZlMQsisZBYsNi0fN914eY8+es5GcY9bBBgd\n62huYuB0AC4YdnICAgEVW8jbfWAw7DsSarWK/NxfIrW9R4dMi1rhhVuMadOEItYHcz3nKm6dhjCu\nhgH6aUMnqHBLFLj/uY3RfF0pMvpuKdn4u7Xcir7B7bzPsHaVs3PT5gn19o6Flb0V7fQbyVdqGcDO\n/skS8LBg4ctYnO8MRKPRcPTAp+h0bYh6GXMiYpm/cPhEmrEQRZF9H3+AnV0T/oF2HNmfha9/HAkp\n4x/qLo74K2KeX5vrV68RNV+OlZXhwSqTSVm3IYDqal+Wr3qypAnHQ7jMGjz7gRqj42dvlWHdafyS\nIUGCpmP0EGN+3q9Yu6EPudxQlNTVPcCBnF1I0l8hbMCe3p5ujvz0c/qKrJHa6Qla70DKZuPe34iY\nBWj+tYDiA/toKe+kW9uIj9dsFIm5rHr7Od7LPwBfkmiWOhoqfcMWzids4fwJfCfGT/zKVN7dvYfA\nolcHXwLqwj/j9XUvjnGlBQszG4vznYF8suuPJCYrUCoNOaUb17JQKJSERYSbtM75s+eYG9aLh6ch\njJucZkdu9gX6++PGvQN2cZtDQ/09vH0MYefKOx34BZgnFFxXW8uChXZGx2ztlPT1dppl/ZlIuGz4\n7nD2iki+F3UW55shg8c6ZTUExhpXxd6y6sZGMeQJ5XaVyOVDikcO9lZ4WpUN7q4v/+1BPE6/gssX\nTQ1Ntyq47HSOmIxEo3UXpCxhQcrIoc3QF5ypqyjGpc8g7NDoeo5FW0JGPHcqkMnlbPyPFWT//hNU\ntUqs/NS88Fb6EyddacHCl7E43xlGV2c3dnadKJVDD9X5C905l3fBZOfb3HSXkHjjytrIKCeuF14j\nPiF+XGusXLOO3KwsKvNuAxL8AxcTu3SpSXY8iqVJSWSffoeliUOf9VZJC+Hz1pll/ScFmUzGSz9x\n59A/vo9wYyEDnndRPt+A/bqXYMBwzoM2JT9PYdCB75ENF3uwFWT4eQrcvlPKwFUvJA91EzqqQqnO\nusGidB1trY04O7uP2dObtDGNIu+r3D7zGYJMJPH5CILD5kz4sxZmX+bmB7WoG2RYBauJ/7MIQueP\n/nvt5uXJ5v8zvuEGvT1d5H2SjaZHZF7GXELCw8a+yIKFacDifGcYarUauWJ4uFEQTJf8k8lsUas6\nUCiHfsy1NT1ELQw0aZ2U9HRgbLF5U3F2ccLDM5acrEsEBCpoqNdgZzeX0LlPRjGMOVmSPodFZ0PJ\nulNBr94dUZEyrCVpjn+N0c7Z2tqfzo5OHJ0Mx2pqutC4hVDbKDKgDUAquTnsPl0Uc+l8Dr6+Wqrv\nSJHKklgUM7pji4xfRGS8cWuPKIoMDPRhZWUz7grcpoZ6rv+wB9+WL0LGDZDdtJeAj0LMspNtamjg\n4Lfy8K94ESvknN97hbpvZ5H8onl+d1uaGxjo78fXP9hSdWxh0jyRzvd64XUa6utITE7G3mFm9Eya\nCzd3V1qajXcj9fXdeHpHmrxW+opV7P7gV2Ss8EShlNHS3EN7uwvevt5jXzwBtFotJ48eQaVuQxSV\npC1fg4vr6NW6ialpqNUJ3Ltbx4LFXtjYmL9o50lBIpHgE+xPbaNInxojUQ5DuNn4gb9h0xZOHD1M\nd1cNIODpHcbC9KXUNooobWyxjm9Gd0QzWKzUYJdP5Jq7rFxr0F+OWgCFV/Kor1uAj+/4X3gKTp7n\n5vsNiHVOSPw7WPCmP9FpY7fSFBy+jE+LcQGZd8U6Lp3KJWndinHf/1Gce+88QRVDOs0ePYu5vWc/\nCS/okEqHRwnGi2qgn73/tA8xfw5StQ3q6N2s/ecUPP18J22zhWeXJ8r5qtVqPvrTr4mar2DePFtO\nHv0NvgEJxCcmjX3xE8TKtds5c+ITZNJudHopLq5zWbVufI35D2NjY822V79F1qnjaDW9uLpFsHWH\n6euMl13v/ZbkVGusrRWIop7Dn/2OF7d9Azu70Se4KBQKQmcHT5ld00FDXT3nco8jCAOALWkZ63F1\nG1vZ6FFFWSAMyxcLgsDq9SO023j2U9t4j8gfrqTC6TN6ihRIbHXI55eQvsIQ4tdodNy83IKblxVF\nJVnjdr5tzU3c/Gkffs1f7JY7oPAnR5m1qAMHB6dRr5VZSdCjNapcVgs9WNvbjOveY6FuGiGE3ujE\nQH/vuHtyR+L4/36O++kdSB88Li8v4PTPd7Pj51snvKYFC0+U8z1x9AjLMhxRKg1/ZIkpPmRnnmdJ\nXDwy2RP1UUbF08uD7a/9hVnWsrGxZt3zG82y1mjcqagkKEiLtbVBAUsQBJYt9yTnzMnHcv+ZRH//\nAKeOvc/KNX6AAlEUOfjp73nt7b8Z1w5spKIsU3jgwPvUSjb+tcFJ3rLqprG0h5aWGzRXiWT+2ywc\ny79Ov00VLRFniEvQjutv6NLRC/g0Gzt8n4ZVXPr8czK2rR/12sRNqXx84CCBVQabRERaFh7j+aRX\nJvhJoaTgGmUnq0EqonJsRYd2yEkCQnAzNpOcodtTqsDmS4/K/vJnN0JjwTw8UR5Lq+1AqTSWN/T2\nkVJfe5+AINNEDSyYl8b6Bjy9jB9IcoUM7SiqThNBo9Fw7PABtJpWRBQsiE4idM7EC4CmgpzM06Sk\nD2kSC4JAQqIL+XnnSE5LGeXKqSVpxSLO//4id94Nxa/c8HJn3+eNc0E0Zz46xqrXNoy5ho2zNV30\nomTIoanoxMnFbpSrDNjaObD6p4s4/94nqO8rUAYMsOVr6yY0OxgM83/v/sQZ155NAHQ6Z1Ee+d94\nla3FSuNBU/BpEv5i7qTzs1InzbBjMueJD5GwYAGeMOcrYI1erzH6Y21p0hGXOPUyfBPh8oUL3K26\nAehxdA4kY9Xqp7ZQY1HsEg7sPUva8qEQYlVlO0Eh5g1z7/nwDySnKlEqDQ/7SxcOoVBsJSAoYIwr\nHx8atQqFwniHa2sr5+7dnmmyyIAgCCxfvZWKvze2TY417aXjm7SzdG0q7+7bQ1DRTgQERESaog+x\nfvn4dq9+IUFs+UGQiZaPzO0DrXj3DPWs+7ano1zYxoK/7aej+Sqrk9eZpZAr+qW5XCzMxrs5DYA2\nmxJmvTB6iN2ChbF4oqYaLVu5juNHG+jrUwNQWtyMvVM4VlYzr+fv0vnzqAcuk5RiQ1KKHe5uVfzn\nj3/CzetF023alGBlpWRO+DKyTt/nVmkT5/Lqae/wY0G06eIgj6KluRUXl+7BtANAbLwXBZdyzHYP\ncxCXkMLli8bziPPP3icpLW16DHoIbx8PZH7NRsdERBTu49NllssVbPnPtfRu30dr6kF6d3zCS//5\n/KQKmiaKtn3440vbLmXu/CjilqeZrRd47qJ5pP/aj94dn9KzZT8RP+s1WwW1hWeXJ2rn6+DowI43\nvk32mTOo+rsJn7eO2ZPoOZxKqiquIpW2UHOvidaWHuzsrVj/fAgd7Tm8+9uTvPTq15+6yt5FMTEs\nXLyYupoG4hJdTZayHIvenj5s7YY/5AUmJ+g/Eg3197l84Sw2NvakLEsfcZpTfl4eTferAAUpy1bh\n4mqQh/TwdMfbL4nM0/mI+l5E0Y750auxtTVPYdFkUCqVRL7RS8UPb+HYH4YeHfdC97Fxx/jD4c5u\nrmz82+lXmLKe049YJQ4qX+nRYROmmpJ7+YeG4P83j09cxMLTj/T73//+9x/Hjfp17WZZRyaTETp7\nNnMjIsdVPTodiKLIZ598wKo1cwmd40FVZQtr1kehVMpxdLIiZJY1OZnlhM+Leuy2tTS3cPzoZ9wq\nvkJdTSNBISFmDYULgoCjkwNyufnf6xydHMjLPs+s0KH8Ym1tF7b2kfj5+5vtPvm5OdwpP0ZMrBV2\ntu0c/uwMgcERWD/0snRg38e4u98jLMIKH18tJ49l4+MXhs0XDvbO7dv09dQxe64dAyoNPb0Cc+aa\nJpIyGVr0Wlq7HHHTGnZ/LTI1/QOdqG20pCWFoIy7RYXzWVrib7P571fi7Oo2xoozD5/5HhRUHKC/\nSUe34i69ydm88A/P0dfbjV6vRy6fuvGX46G08DoFRy8xoO7Gw9f7qU05WXg0/l6P/pkLoiiart4w\nAVpVd8Y+6SnhwrnzWCkL8fJ2oL9fzbWrNSxNnGV0Tv7ZHja99LXHaldbaztHD/6WjJW+CIJAZ2c/\nhVckbHv1rQmt193dQ9apY+h1fdjZe7Js5coJF8+Ml+qqavJzDmFl1YdaLcXJZQ6r1098mk5ZaSk3\nCvOQSNTo9XasWreJowffIX251+A5oiiSfxZe3PYqYPjcp4/92kiZSxRFsjPVKJQKNOoOau9VkLFq\nDl7ejgDcLmvF1WMFEZERmMKDMYS1jaKRutWDr9U2Gv58R/pa+ZdmBz+Qp3xw7kjnPIk01N1FRCTz\ntzk0nRYQVTL6lPeZvcqTzf/40rR0Quz78R50+xfhop5Lp7wK9ZpcXv6+ZSTgs0bCwkc/D5+osPOT\nQlNTPTExhgealZWcnp7hoTBRNG9IdjzkZZ1k+QqfwQeAo6M1jo73abzfjKeXaUVrfX397Nv1P6xY\n7YVMJqWzs4aP3/8dO17/s6kwfZCg4CCCgr+FSqVCLpdPytk31DVQcvMIyanegA16vZ49H/4vvl/S\nThAEAUHoG/x/a3Mbrm7De0rvVhWz882FSCTOQAwnPi/G3t4KWzsls+e6cunCTZOdLzDoYCfKw2Id\nE6W7uwO5TIGV9fSHzr+Mt28gn/7kE1wPb8cTw05/oL+LygOnOOF1lHV/Zv5Rh6NRWXoLzcF5uKnn\nAuCoCabrmEDR6itELbWMB7Rg4IkquHpSWBC9hKIbTcAXYVhHa65frQVAq9Vx5lQdS5NWPna7RNTD\nnJWHp5LmpiaT18o+fZLlKzyRfaEv7Ohojbd3H3er75rF1mtXrrDno1/zya5fsuejP9LZ0UlnRxda\nraHFQ6lUTnqXffF8NvEJQztciURCWISCigpjZ6XX69GLQ04nIMiPmrvGrSaXLlSxYnWIkU3LVoRx\n+VI1AD3dA9jYOppsY7jMGj9PYdjOdqyvPeCWVTcK7mCtOkug2+1Rzx2J1sYm3vvWx+zbcINdL5zl\nkx/tQaczf459snQVKJAxVGBlhQMCUjpvPP6dZsXVctz6jac9OWiCqCtueOy2WJi5WHa+Y1B+qxyd\nTk9YxPj7BQODAiktCuXC+XJCQ+3Q66243+jMwAUZEok1z23682mRxXR3D6LxfjGeXkM507LSfra+\nMtfktdTqHiPNaABffztq7tYQGDSkHa3X6zn46R7UA7UgiIiiM89vfnXUYqyK8goa7+eSmmbYjd8q\nbeDd3/4rc+Z60t0j4OIWwYrVExu+UHDpEveqiwGB+/UtCIKX0detrWV4ekdwLvcecQledHWqOH+u\ngy3bh1IEEomEqIUryDx1krnh1jQ3qbh5Q03U/C/1Ocul6HUiGrWWnKw2dr792oRsHs1ZjuVI7xYc\nx2ngDLP8Fdy+qsHJZR7hJnzvjv0kE++87YNFTepPejnpcZQ1b09skP1UIchHjg5I7R9/P+7c2AjO\n2l6lr7ebftoQkNAnaWZN5OOv8bAwc7E430fQ1trGoU/fZfZcGTKphPd+d4TVG17By9tr7IuB1euf\np7Oji9tl5SxbORcHx+nPqyWkJHFgXw3V1XW4ucq4e1fHvPkZE8qJ+QfMobbmMn7+Q7J91wvbWL9x\nsdF5ez/6gEVLNDg4GPSktVodBz/9iG2vjJxnbmtt58C+93jlNcMLgUaj4151G9teGZp3W1F+h+Kb\nxcyLmmeSzblZmcikxSQkGqqSi29KOHakhDXrh0LBJUX9vPrWNjo7usjPy8XRyYk3/ixx2C57fvRC\nIqIiKS0uI2SOFT7+A5zPP8zyFUNiLwWXaunvd+f6dQe2v74N+RgThMzFgzxxT3srnvpMUpcZ4uiz\nZkPRjTKqKucRHBI05jp6vZ6BYrtBxwugwJaW61Ni9qTwSpPSV9KMDYYXtg7uobZpJurFsTWnzU3g\n7FCOLvoZznnPEYShD1mj76cs8wDz4x+/PRZmJhbn+whOHdvPqrUeg7vdwGDIOXOQl14Zf07T0cmB\nJXEzJ8cjCAIbt2ynu6ub5uZWlqYETDh0uzg2hgP7Kqivq8XH14o7t/sJDEk0ap+qKC+nrbUIB4eh\n+b8ymRSBxpGWpKGunsxT7+PlNRTWLLpRx5LYIKPzQue4cPHCdZOdb0PdDVLTh6p650V5UHG73hbE\n0wAAIABJREFUl5ysZgRU6PR2pGVsRRAEnJwdWfvc6IpPMpmMivIbaDV3CAiw515VDR9/0I5/oBMa\njYKAwHjWb0wzycbJcqW9hVPZBchkUuz7W1m/1PhlMXK+B5cuXh6X8xUEAcFey5d/XFL7mRd2XvX2\nek5bHeP24Sb6O1TI/Lp5/q/WMjvK9By7OXBRzMKVoYiSHGs6z1uj1+unvCjxUTQ3NlBVXE5EzELs\n7E1PgVgwLxbn+wgkkh4EwTg0KhEmX7gyE7B3sDdL2PuFzS/T2dFFbU0tG18KHdYLe+1KLo6OI7V7\njCzIcP7saZav8ON2WSOlxQ2Ez/PG2cWGlpYeXFyHhjPodHokEtML1gSGhyBdXB3Ysv0vx3W9KIqc\nP3uO1uZ6XN19ECQSAgJacXf34+L5KoJCnKm804KtXQKr1z83qcrWh6ucHzBSRfPD9N6q5R8Lq+hL\nzkDU67F677fEBEnx8R2KTnR3D9Bl4zwuGwRBwG+1nO7/rcNea9g9N7lcYNHGWWNc+fgRBIEVr65l\nxavTbckX6Ef42eumr9L54C8+pfOgN04d0RR7XiTkTQmpW5dPmz0WLAVXj0QvDncaojjzlLSmG0cn\nB+ZFRYwoQiEIahydrLl3t23wWEdbH1bWj9DhFgxV4bPnejIwoOHUiRJKiu6TdfouGvWQ48zJbCA5\nzfQRdHrRgYc767RaHTD+HcAHf/wN9nZFxMSpsbcrIvPEflzdbDl6+CZL4gJJzwhj5ZoIcjI/N0tL\nSXmNPwFd8wjomkeferh8Zk9nDx98t4j33igl54+X+P2VcvqXrUKQy5Eolai+8k3+8FktapV28PPu\nP9bFAhP0pVe9tQ6vfymjY81ndG38lJif2xIeM3/sC59x/FId6JYPTabSocU+tndadr1Fl66i/jga\n744krHHCr3EVlb+Hzo62sS+2MGVYdr6PIDxiKVcuZ7I4xiCQf/N6M0Gh8dNs1ROG4ED0YjuuXa3h\ndlkjgiBQXTnA937wHyOeLpc7o1IZ5COjFxuczZlTTXz3n/+cE0cOotN1IopK0le8ipOz6WGz1etf\n4tD+93BzVyHqoK3dmhe3ja/H+dqVQiLmgbuHoVjN3cOOFasD2bfnClu2LR6UvPQPcCE2zpemxhY8\nPM0rXCGRB1KquUu4zJre3j52P3edwCtvI0FKzycd1Cb9DDKGzhcEgfqglezOVqGkDXuNJ8Erv4ZM\n1jZs1zwaCevTYPSBRU8UnV1tXCw6T6hvKCGBphcbjoek59M503OC2pOX0Q9IsF84wAt/9Xhbnh5Q\nfakWJ7Vx+su7OY3C7JOkvbDGpLXUahWf//owPSUKZA46ojYHMy/efBKyzxIWkY1RuFt9l8KCc4ii\nSNSCOELnhE63SY+FK5cuced2AYKgARxZ98JLE5LC7OvrZ+9H7xASImJrJ6OkqI/EtM2EzBpZpk+j\n0bD7/Xfw81fh6mZF5qla7B3scXCQoxdtWRK/klmhkw95tjS3IpVKcXYZvzj+4QP7iYnpG3b8R/96\nnH/459WAISx9Ib+S9rY+tFpnFsWksyQuziTbHhbO6FMHPFIk45N/y8fhx68jYyjicM8qn7NHdMiC\nhxqVww/m8p30nUb3eLDOl9d/Fjh+5TSHJE2o4hcjVFYRVVTHN1e8MW152MdB3qHTdPxLHFYPRXma\nra+R+J4NgbNNe6Z99A8f4Xps2+BM5kaXC8T9wm7acuszndFENp44ecnHiZOTE2ERUYTPm4+Lq8t0\nm/NYKL5ZRGtzLktinQkMssHPHz4/dIEF0bEmryWXy1m4OB6d6IlW60PG6hdG/T5KpVIWLIpFkHhT\nXy9HKmthxSo/AoPsCApWkpN5gfB5sZMW8bextTFZd1oqyKisKMTNfSj3XH6rFZksFBdXFTa2CnKz\nbxMW7sWCaH/mhjvQ3HiHhnqdSdKXLXot99sD0OgchzlGN62SBsGa1i5Hqk7U4XTNOPwr19pQZvMB\nLJmHqNHg+Hkmb8xNx8needg6jiqPQenJp5WcvWfI+Y/rFO4q53bpdbzmufFOxw3U6YkIMhmCuxsN\nXk7YFxYR4jvz8tjmwndWAOeKdmNdOwcZCnol99Gvv0DiptSxL36Irs42iv5DhZNq6Htl1+9HjSyP\n8OTHJ536JDGavKQl7GzBiPLSApYmDmlmS6USXF37aWluxc19YlraIbOCjf4/VsVnUHAQN69fJTHJ\n2+h4YrIHedk5LF9per7XVKoqq6mrqWVx7BKsra2wsrbm4vm7qNVq5kX5UHSjjsuX2vjHH/yE3R/8\nHi+vZlQDWtzchxzmrNnO5OVcJy4hYdz3DZdZg38NtY0itxjamT5QqXqw8x14XqR8VwXO6qGdS8us\nM/zse6s5V3CT5jYJOxJfN9tknyeNSyfO0vyzWfioDVEW/W0duxr+i95fJhg99KSuLlzRluBrBhWw\nmUz8/z5H855cumvUeEU6ErfiZZOVzzq720Az/PepXa81i4ra00jCKDUlFudrwYiRchBSqYBON755\nr6NRe6+GvKyDSKTd6HUy3DwfLZYhEQT0epGHN7l6nR6JMLWj63Q6Hbveewf/gAG8few4/Gk+QaHJ\n1N67w2tvxXC/oZOzuRXMmevJosUy2lrbeXnnVyi4WEBv794RVjRd5CFcZg2e/ZTXGB8PC5Ew9wul\nrfA1S/jpd85Q/cc72DeF0Bt+mfX/YoOjqzPr1y/kVqUexcCz6XgBqrNacFYPFZZJkGJdPgeh5Bak\nJA0e1w8MMMddQVjIzAs736qc/N/cA2QKBelbVk9qDUd3T/oW5yPmxQ/2frdZl+ORMTPnqc90LM73\nKaO7u4fKikpC54ROaIRdUEgklXcuEDLLkA8VRZHGRplJ2s83r1+n+EYeEkk/etGGRUsymD13Dpkn\n97ByjRdgWPtedRUFly6zJHa48EByWgbHDv16UCAC4Mihcna+tcnkz2QKp44fIyFRga2dYceZkm5N\n1pmzyOWugAIvb8fBYQnd3Wo6Ow2SlxVlZ1AqBaNdfV+fGqXSc8psXfd/Eri2yYH2hgY2pYQTZe1g\nUiHV08xI72gSOYS3qyi7W40QGISupwffrDOsfXOm9CcN8cDxTmVOfiJru//TGk789GP6S22QOekJ\nWm9PavxyGJgCA59yLM73KeLk0UP09pYRFGzNicPHcXSOZPmqtSatsWjJEnKzusjJugFoEEVH1r+w\nc8zrHtDe1kFZyQnSlvnwwMmeOfkZOv0mgkKMn4gBQU5cyC8Z0fk6ONoTHJrGrvc/xtvHDrVaR0qa\nPwf3vcvrX/3OlE2HUfW3YmtnvGMMCpZTV2dPfV0LPr5DD6x71XqS0wM5sO9jUpf50NfrxrEjRdja\nKenr0yCTBbHj9amde2tj74iNvSMyec2o52m1Ws4ePENHuQq7IBmpmzOmfOSeTqdDrR7A2tp27JPN\nzOxVPpRll+DSZygE0qLGOq6NDWu2sqL9Gjcu5uNhZ0PG268hmyLlsYd3rhPZWc/EYjhXd3e2/3jb\ndJvxVGBxvk8J1VXVSCQVLE005Em9fZy4WlBKQ1003r7eY1xtTEr6MmDZhOw4l5fF0kRjVaXEFE8K\nLl/Hzm64MpJeNDyUykrLqaqsIG5pwmAV8v36u2x7ZZFRfliraaf4ZgmR801Ttxo/Vuh0KqTSoXs2\n3tewat0a8rJPcbv8Nkorkd4eKxJSNxpUoNAgCFJs7ZSse24+Go2Ou9VteHgtn1RxmI3iHrcIGPy3\nXiOACUMRHiCKIh9970NcT2/BFntU9PH+uY9441evTVmV7/4LhzkjNjFga41Lew8rw2LwD3x83QJW\nK8NRqC9RdfAmYq8E+UI1Md82DDNZuHgRCxcvemy2TBRLHvXJx5LzfQa4UVhAbJyH0bHoxR5cuXyB\n9b4bH5sdUkGCXqc3cl5ajQ4XVxfqaprQqLXIFYZfu8sXG4lespEP/vgbQmZpmTfPgZwz7+DsuojU\nZRmI6IY5BxtbGX19vVNmf1rGGvbv/TXLM7xQKGXcq+5AKgvC3t6OtRs2otPpGBhQGYX03dyDaLxf\nOjiwQi6XUl2pJSFl4hW0D/K+8GBHa9o0oocpvnwV25x0lBh2UgpscM3fQMGZs8SuGL/gxnjZX57J\n0dn2SIKjkAAdwJkTx/n3tNmPdZ5t2Lfi4VuP7XbD7z+JPPJMzEFbMC8W52tGurt7OJudhSCRkLps\nucntLJPB1c2T1pYSXN2GQnyN93vw8g57bDYAJC/L4NCn/82yjKFc7dm8Vna8/hp6fRLHDu1Hp+9A\n1MuJWriWqoo7xMbLcPxi8ER8gg9nc6/S15dI5IJYbl4/RNSCoXxz4dVutr+2eNh9zYWjkwNbd3yL\n7NMn0Wj6CAxKYMPGoV2SVCodlktPSkvlwL56KitqcHSS0tAAi2PXjrirfDDlSTVQB6IeidSdF7bs\nGFEhbCxn+6Ayeizqb9fhoDEWWbAVPWi9mz/mtQ8z3p1YWUstkiXGTv3+nLncKb1FaIT5W1K0Gg2Z\nmTl09PYSFRJCa0c7UfMjcXR5NtoDLTyZWEQ2zERpUTE3Cg+TkOyNXq8nL6eRxNRt4xKwNwd6vZ53\nf/ufLF/pgpWVnL4+NdmZnbzx1W+bbbchiiKFBVeoq71L6JwIwueN/CC9U3GHq5fOIAj96PU2JKas\nxdffd8RzD+z7gPilxvY1N3Wj1sSxOHYR+bk53Lt7FYlEhU5rS0zCakJnzzbL5zE3fX39dLR14O3r\n9cjv+eED+wgL68De3vBiplZpuXgBtmx/3Sw2lGr7kcgDuVWpH8wZNt1v4Nj2Cnzah/o6m+wKSPqj\nw4giC49ysuPdjf1xz6fkJyQbfw+uX+NHC+fh7utjwqcZm872dv59z36aUtPpPX8eqa0tVvPnY1Vc\nzAprBRvXrTLr/SxYMIUExaPz9padr5m4fi2btGUPNIulZKz0Iy/nJMEhX30s95dIJLzy5jfIPHEc\nlboTK6U7r775qlkd7wd//A2RURAT68Cd26fYt7uAzduGV4rOCp01biUqhcIBlaptUJ4R4G5VLwmp\nht7ghJRUEjBNDGC6sLGxHlMJbKC3AfuHRC8UShl6/f0ptcvDy5uArxRR/cFxHBrm0+1Rgs+2AQJn\nD8973rLqnnTIc23SUq7lnaU/KRkAvUpFxP0G3H3N35+978QZWtZtQFNejjI4GOUXL2aa+HiOXykg\nseE+HuMcA2rBwuPE4nzNhETo48si/YLweNs+lEola56bGv3Yi/nnWRgt4OFpeJObNdsFlbqF6qpq\ngoKDJrzuspWr+ejdX5Ka7oKdvRVVle3oRX9cXMc3eedJY8Qwkzg1edCC0/lUnmkCYFaGJ1s/iaTq\nVhkBsxfh4DB+aU1T8fL14Tux0RzJzaZHEAiQy9i68+UpuVerICAIApqaGuwzMoy+plu0mPMXL/L8\nC6OPhrRgYTqwOF8zoReH99SK4sQKZGYizU21xMQah1DCI1wpvHJzUs7XykrJzrf/itzMTHp7Ogia\nlcrS5Kd3ao6bxxwa6ivx9jF8Lzs7+rGxHT6xaLIU7TsHP5yD84BBUKIypxz19wpZumHsAqvJtsgA\nBIUE842Q4LFPnCTuoki5Xo/E3h5tWxuyh/O81VXMmTX1NliwMBEsztdMLFyUTtaZwyQmeaLT68nL\naSJ12dPTD+fhFUBDfeGg0wAovtlM5ILJh4TlcjnLVz0bubllK1aRdfokdypuI4oidvb+rN/43Liu\nvd/QyIWzmSDoCZ+3hLnhj57I03qol5CBoXyuU/8c7hwpYukYm8AvD3K4Vamf0ZW3W9eu5M6He6lb\nmkD3qVM4rF2L1N4eXVsbkeVlhL81/h51C083mVm53GhsRg4siwonfN70DoOwOF8zERYRgX9gEHlZ\nmUilMrbu2IGV1fTI+928foOKsmuICEQvTiLYDG//sfFxfPSn62jUHQQEOXGrtIWubi8Cgsy/a3va\nSc9YCaw06ZrbZeXcLDzA0iRvBEGgpOgYrc33SUgZ+eVH7B3uMHUjHHvAk9pTauvgwL9+7U3yz56j\nOTgASm/QpodgF2fS3nhlus177IiiyN4Dh7nZOwCIzLezYcvz6x9ri9dM5JNDRznu7Y9ktqFItOj6\ndb6m0bJg4fRF2SzO14zY2tqwev30Dj7Ny85C1N0YHI5QeOUzuruXM3/hgkmtKwgCr7zxZ9y8fpMr\nBRXMjYgnLcN00YSqymounz+OIPShF62JXryMOWFTM1P1aeLalWySU4cqhSMi3cjOvPrIYjTpgj50\nJVqkX/yJ69BgN1817LyHne5M3uGOhkQiISklebrNmBHsPnCY03MikDga6k9OtLfDwSNsfcbz3hc6\nupEsGhIb0i5YwJm8bIvztWA+6muvkZo+1BcbvdiD3OzzE3a+FbcruH4lDwQNNtaerFq/gagFURNa\nS6VSkZe1m5Wr/QCDIEX2mQMolC9z+cJpJEIver2S6CVphM6ZM6F7PK0IEjVgXFcgEYY70wfE/d0K\nirp2033ZCQQQ41tI/O4abimH73BngtNV9ffz0cGj1OjBDj1rFkQR8YhWNguP5mZv/6DjBZA4O3Oj\np5+t02jTdCOKIn0j7Pz7mN5ogMX5PmUIaIYfE9QTWqu6spqSGwdITPYC5PR0N/PJrj/x0itvTmi9\nvOwcklKMVbgSkj3Zu+t/2PFaJIJgqHDOzfoMZ5ev4OpmEUl4gKi3GzaKUae3e+T5CitrFv10AxrV\nAHOCJSitHp/gy0T4z4/2UJGxCkFmeCRVXrzId22sCQgOmla7RqO3uxu5QoFCOXOmR41UTW++2UhP\nJoIgEKDVUPnQMX1/PyHy6XV/Fuf7lPHlB7JhFKDDhNa6eimHhOShHkk7eyusrOrp7urG3sF00XdR\nFPnyC2hxUT0ZqwKNclKJKd7k52WyYePmCdn9gOtXr3KrJB+pVI1OZ0Niynr8AvzGvnAGsnr9i3y6\n+3fMDpNiYy2n6EYPSemP3s8M7WZNn2z1uLlfU8ttH38ksqHHkToujlP5ubwVHDRtdj2KxvoG3jl+\nmhoHRxRqNQsFkbe2bZ4RedV5VkqyuruR2Bv+PvVdXUTZzJyXg+nirTUr+O3RY9x1dUOu0TCvv5et\n26c3HmBxvk8ZaRkbOXF0FyGzJKjVemprZWx++SsTWkuQ6ADjwQC2tlJ6evom5HxT0tP4ZNcvyFg5\npHZ1/WozIZvdjM6TSAT0uuFDGEyhof4+1VVnSE0fyvOcOLaLV9/8m2Gyj5V3qlGrVMwNnzMjHqAj\nYe9gz+tf/Q5lpWX09faz4435UzYU4XGjGhhAb23Nlz+NZob+LH53/DT3Vhpm46qA811duB89zvPr\n10yvYcD2Tc/BZ4co6jekJKJsrHjJDPleURQ5euIUN9q7kIoiCQG+JCclTHrdx4WHlyf/9NZOOltb\nkSuV2Ng9Omr0uLA436cMbx9vdr79HSrvVKNUKFm+euJyfs4ugbQ0V+DmPqQX3dAgkLFmYjNqlUol\nS5M3k5N9ConQiyjasH7jm1y6+DkrVg3t0K4WNLE4dnJtWpfP5xAbZ6xstDjGgcsXLhOXEAdAd1c3\nn+75A8EhIgqFlA/+cJiM1dvx8TOvBKI5Ga296EklIHQWvtnnaHxYNrSigvjH0CdsKgN9fdTYGj+4\nJQ4OFHd0MTXyNqYhkUh45cUXzL7u3oNHOBUUihBpeFG+U1WFNucs6alJZr/XVOLo6jrdJgzyTDvf\nm9dvUFJ0FokwgF5vR0r6erzNrD07HQiCwKzQyT+4Upcv4/D+FkqKq7G2hs4uJcnpkxtmP5L0pLWN\nDdmZx5FKetHrrZg1JxG/gJG1oMfP8F2TTqtHpRoqUjp+5FNWrnYZ3EEGh0DWmQO8vPPPJ3lvC6Yg\nCAJfX7WM986cpE4ixV6EVB9PFi5eOt2mDUOmUKBQa4bNji+uuktpSSnhUzA4YiZwpbsXwW0oQiUG\nB5Ofm036NNr0pPPMOt/7DY1UlJ0gNc2bBznRY0c/4LW3//apCedNFkEQeO7Fl1Cr1Qz0q3BwnJrh\n3gaH/BdmXTN07gKyznzIsoyhneKF/Eq8fIYeIILQjURiLGMplfSY1Q4L48M3wJ9/eG3HdJsxJjKZ\njMVKGTltrchcDLuovitXkCYl8dnVG0+t81WPUMk1vLTTgik8s873Un4O8QnGYcklMY4UXCogNj52\nmqyamSgUimEj7/KysmhqvI0ogo9fOAnJpvVZlhQVc6e8GFt7Z1LS05HJzPurqFYNYGMj59TxEuRy\nKSqVloSkWdy5M7TzFcXhY/xEhh+zYOFhXtuykXP/9mO6/PxBFFGGhKCcPZuW+rrpNm3KmIWe6zod\ngtRQA6Lv7WWuleVvZTI8s853pEmKggCi/rFMWHyiyTx5HCfnKhLnGHbC9+7eICdLTWr6cqPz+vr6\nOX7kMxC7EEUlsUuX4x8YwOHP9uHsVE9MnAvd3Xf50zs/55U3/9KsimARUeGUlZxgxeqQwWMtLb04\nuwxVO0dEJlBw8SRL4gw57Fulrfj5Lxz3PQYGVLQ0tyDqwT9wsmHypxudTkd+3jk6untYlpqErcPE\nKvBnAoIgMDckmLK0ZUbH3cWnt6nnK5tf4H/3HaBCkCLV64mUS3lp2+S6EZ51ntl5vg3197l0/kPi\n4od2v8c/r2PnW5aw80jcKrlFWek1lApbWlvLWb7CuOgqN7udrTu+aXTsT+/8goxVzshkhrfl7DN1\nxCVu4+a1vcTGD1Uhq1Qaim46s+558xaKXDp/nsrbuYTPs6e+ro/ubje2bH/NqKL5XvU9rhbkIYp6\n5oYvIiJy3pjrNt5v5Mj+D2huqmZBtA9yhYy6Whlrn9+Ju4fbmNdPJQ/m+c4kOtva+PGe/dxPSkFi\nZ4dVfj4754QQGzN8pOGTQnVlFb/KyqMzJQ0kEuzycvha3GLCw8Om2zQjtBoN7396kNs6PXJRJN7D\nlbUrM8a+8BE8cBcztStgpjHaPN9n1vkC3Ci8RmnxOcOgdp0NyWkbHjn0/Vnm5NHDKK0qmRvmysCA\nhn27r/HcpkgcHIamNuXltLBl+7cH/19WUkZr6wlCQoaEMnQ6PUcOdRK/VIqnl/HO58J5kRc2D58N\nPFlUKhXFN0rw9ffD08t97AvGwUfv/gqNpp4VqyOQSg0vaqIokp3Zy8s7v2aWe0yUmeh8f7f7Ey4m\npho9sFUff4y7jxeeosjWhFiCZmBl81ioBgbIzMpBr9ezPD0VK5uZ11P9mw/3ULA0EckDIZD6OrZ2\nd7Biedq02vWsMJrzfWbDzgDzoxcyP3r8YcZnkYEBFV1dt0haaNipWlnJ2b5zMaeOl7BqbSQAGrUW\nQWJcwt/R2YGjg3FOSCqV4OrmRMXtBiPn2909gK3t1Lz0KJVKFsVEm229np5e7Bz66e2WDTpeMOwE\nJJIus93naaIZybCdksbXl464OLqUSv77+Of8P38/ZHL5NFk4MZRWVqxZM3OncYmiSKleHHK8AD6+\nFOTeZsX0mWXhC55p52thbJobW3BzNxbakEgktLZAXk4togh6vSubXtpudM7imMXs+TCH5SuGeiJv\nlbYSOX8VjfdruZB/lZg4L2rudVJeJmHH66ZN+ZkuFAo5GrXwhXLYsK8+dnueBFxEPZWiaOSA9X19\nCF8U8bUlJpOXnUd4RBhHz52nXxSY5+FGevrYs4efdnQ6HVlZudR0dBLo7ERaesqoaTFRFPnk0FEK\nu3vRiiKd3T1m+a0Uv/TzszB5LM7Xwqj4+HlxLldP2EMdFCqVhtlhsazZYJhDO1KlskwmY3HserLO\nnESpHECtluPlHUVYRBhhEWG0tS7mYn4+AUGL2fnW2HnWmYJCoQDBC2dXNdcLa1gQ7Q/ArdJm/PzN\nt8N+mnhxWSp3Dh6lLX05gpUVvfn5yDw8Bh/mAtDW1sqPsvPpS0lBEAQKGxup3XeAVzebXzDiSUEU\nRX76h/coS0hGGhZJXns7BX94j799+/VHOsKDR49zMigU4Qsxib6DB5Gr1YMvOvqGBha5j19oIi//\nAsfuVNMuSPDU63gxej5RUU/O3+tM5pnO+Vp4NHq9nvNn8+np6cbGxpqGuovExntwv6GHW6V6tr/2\n9WHtRwBXCy5zp/wKoEEidWb9C1sAg9N6Wt6cRVHkxJHDVFcV09PVibOrJzHxy5gfPbmxjeZgKnK+\n+48c50J7B/0IBOl1vL1hNY4upg29UKtUnMnMpqWtnbPt3ehfGHKqTseOEmxrTWGKcfWwVV4uP924\nbsYPhRiLgb4+srJzsVZakZyWjFQqHfb13+8/RKUoQYGeWGcnNq1fzaXzl/itzAqJ91Bxor6ujm9I\n9EQ/oljtB7s+oTYlbfD/olrNwLvv4jN3Ngog1tWZ59aML8rUUFvHDy5fQxcz1Hppe/oUP9626Yn/\nmTwuLAVXFgCD08jJzKajvRaZzJZlK9dgY2M97Lz2tg72732HpQlO2NkpyD/bSPCsFLq7u/Hy8SVy\n/shvvkU3btLUkElEpOHNWqPWkpszwI7Xp7cI6VnC3M43MyuXXbaOCD4G5TdRFJl16gTfe3PixXGl\nJaUcunqDNokED52ObamJ7Dl/idJE4zCzvrCQn8ZF4+xhnkK56eDmzWJ+f/U6PUkpMDCAW14uf7Nx\nPe6eQ9O9/vNPH1Gcvnywh1ZsbGRLWxOdPT2cihmu8rXq8nk2b3xuxPv920d7uZdqrDvlkJXJz159\nyWTbd316gMzYBON0QX8/L98pI2O1JWs8HkZzvpaemmeI3R/8AQ/328QvFViwoIvdH/yK/v4vC+VB\n5slDrF3vjYurDQqljLTlvlRVXiRj9cpHOl6AstKCQccLIFfIsHfopqtz+AxZC08G1xqbBh0vGArL\nqu0d6O2e+M80PCKc777yEj/evoW/fnUbvgH+hNhYo//Smt4tTTi5T23r1lTvPT4rvEHf8hVIlEok\njo60rlvPnjPZg1/X6XRUyOSDjhdA8PTkWksrSyIjoKTEeMGbN4gdZQD8IjcXxPv3h9ZHZZsOAAAg\nAElEQVTv6SFSMbHsopVMBhpjHSuxpwd7++kfSvA0YMn5PiPU3qvD3aMLF1fDG7dcIWP5Cg9yTp9k\n9Qbjt2hB0osgGL+xSST9w+bJfhlhhGmichloNE+2EJ1Go+Fi/kXs7e2YH73gqQmfjwfZCJ9VqtOZ\nXZHsuXWrqf9wD9dt7VE5OeFVVcnO5Pgp+14fOn6SvMZWeiQS/HRaXluWgl+Av9nv0yQYh5gFQaBZ\nMK6SF0Z4AZAiEDI7lGVFJeRcKUA9NwxFaQlpgn7UGcfrVmWgP36SgrJSdCKEWSl4eYJ589XL08jb\n+xk9X0xwEkUR7wv5xH7trQmtZ8EYi/N9RrhXXY1/gLFDVSrlqNTDdzB63fB8jl6vHFN8xMcvjJp7\nhfgHOAKGP9aWZiWubi709fVTcrOYwOCgaReiMIXyW2VcOn+QmFgnenu1vPvb07y47as4Oj25Ck2m\nkDx7FiWlJejCIwDQDwwQoR5AaT08XTEZJBIJf77zZbra2uhq78B3WaJZHa9eryc7K5fq9g40zS1c\niohEstxQIFcN/O/xY/zbW6+a3dm7iDoavnTM9SElLIlEwjxECgYGkHyRRxWqqojzN0QbXt74HCsb\nmygqLiEyMRbXcYTgN6xeyeSHCIKNnR1/nZHGgdxs2iUSPPR6tr206Zl6+ZxKLDnfZ4Te3j4+P/hf\nJKcO9dM21Heh0kQTn2CcV2qoq+fU8fdJTfdEoZBx5VIj7l5LiUtIHPM+Z04co7mpFEHQotPbs2rt\nVopvXqex4TLhEQ7cu9vLgMqbTVsnJqIviiI3Cq/T0tzM0uSkEXPW5mT3+78ibflQcZFeryf/rMjm\nl1+b0vtOlKkouLp4qYDs25X0CxJmyaW8vHGD2Xe+U83Pfv8nimOXInVxQdfZSfepUzi++CKCICBq\ntXQdOEC6nRVp8bGEj0PlbCR6OjvZd/w0rYKApyCwed0qbhTf4k+Vd1EvTUDUaHDIzuQ7GWn4BwYM\nXqfVatm1/xC3NVqUQKKfzxM3qm+6EEWR3QcOc723Hz0QIZfx6uYXhhW1TReWgisLAJzLyaau5hLh\n8+ypuddL/4AXm7buGPFNtq+vn9wzp1FrVMTEJ+Ht4zXCimPT3dXNyaO/ITFlKG9YW9OJIIllcWyM\nSWv19w/w8fu/Zv4CJW7uNlw838TsuctYFGPaOqaw96N/JyXNOByZf7aLTS/NzLGDM1Hharq5UXiN\nX/ZpkQQOfV80TU1oamqwioyk89AhHFavRmpvj3j7Nss7Wnj5EQVNj0Kr0fBPv3+P5nUbECQSRK0W\n38+P8P2vv017Syunz+ajlMlZtXxmKWE132/k+JHPmT0nlLjkpHHtagf6+ii6fpPgkGBcHyocmw72\nHjjM8dAwpE5OgKF/PKmwgDdeenFa7XqAReHKAgCJqWn098dTcrOEmKUBo4Z/bWysWb1h8sGrK5cL\niF5s3Ffo5+/IpQsVJjvfk0cPsmKVK3K54a02Nd2XzNO5RC9ZMmWhML1++LAHnd4ipjFe+np6kEgk\n0+pwKqrvISyJNzom9/Bg4MYNevPzcXzuuUEVKGH2bHIvtLC2rc2kdqozmdk0pi1D+kVqRpDJqF2a\nyMX8C8QnLmWric78cfDf7/yR3I5ubDIyOKPR8N7/+zn/9+3XcB2lyO1Mdh4H6hrpmTcPxcWrxPX1\n8OY0Dlgo6ukbdLwAEhsbStXaabPHFCzVzs8Y1tZWLI5d9NjyrkEhwdytNpZd7O9Xo7Q2PWeqF7sH\nHe8DXF1F2lrbJ2XjaAQELuZ6YSNg0KbOyaxjyf9v7z7D4jqzBI//bxVQZEQQSiCScs4gMkIIZVmW\ns6V26rZnd7Z32z07PTs7u8/M9uxMTz87z+6EfXq722O3Q9uWbVlZsgIZIYRyBEmAhDJCZBBFVVH3\n7gdkpBIgUUBVYXR+37jcuvcthTr1vve858T2vzD9s+J+czO//uhT3v8um/d37eefPv4jZpPp6S90\ngLj5c9GdOW1zzFp6gSkNdfjevGFbfhEwRkVTVXnVrns03L+Pztc2C1gJCqKmrt7m2P2WFnIOZHOl\nvMKu6w+20nPnKWhpI+C113APDcV93DgsGzfx0bZdvb6mrbWVrXdqaE9JwS0kBHXefIqiJnC0+KgT\nR25LoYcv3T+QR9ISfIVDRUZFcueOLw31bUBndaycrFpS0+0PYKrV0G1rSGOj5tDkp/jkFCZN28CR\nYj0njnuzbNV7REVHOux+jnD7+g2y9mdRf6/Waff8cMceypdmoi6OpyMhkfPJaXz2hA92Rxo7PpwM\n1YK+pARrSwv6kydY0tLI37z/H9gwdybWx7Y4+V26yKRp9nUnSl4wH/3JkzbHPI4Uk5L4MJ8ir+AQ\nf7FrP59HTuBX16v53x9+itVq7f8bG4BTlyvQBduuSCmKwhVz7zsTThw7gXGObRU33bhxXLh12yFj\n7Iu5gf6otQ//XastLczyGrzWpI4ky87C4V790Y/Jz8nl0qXbeHgE8dobr2Iw2P8fJCV9Bbu3fUDa\n0tEYDO5cOH+P0FGzHJ78ExUd+YMLuN/7cPM3lPgFYp0yhS2FJWS469iwZoXD71uFDuWR7HjFw4Or\nVtf1yn5x7SqW1tZx7tx5psfO63pWuSwjnQt/+IyLk6fC+Ajcj5awMjQYLx8fu64/dnw468srOZCb\nS2NwMEG191gTFd61dG02mdh+7RamtCWdM57Jkzk/diz7DmSxygXNGcaMGIF6r/uKUYiu92ljVFQk\n+ouVMGNm1zHVaCR4EPtw22vdyky0Pfs5ef4sGgrTvQ28PASX+HsiwVc4nKIopKYvefqJTxEyMoSX\nNv6MguyDmM1Gps1cw4RJEwZhhMPT2VOnKQ6PRImIRAdYFy3iwInjJN2pJnRM/xLo+soTjcc3sXm6\neDkwMCSY5LQUm2N6vZ7//OM3KT17jitnTpCQlkhgSDAmo5GqikrCoyLx9u1bUYnl6aks7eig8V4t\ngaEjbTJur14upyFmgk2TA72fH9da2wbhndkvJTWJ7YVF3CsuxicuDjQN0759vJbae5Z1WGQEswuK\nOFk7Bn1ICKrJxOisAywfQLWzgVIUhfWrl7PeZSPoP8l2FmIYeTTb+fOtO8hbFG/ze03TWHfyKGvW\nrbb72h0dHeTlFtDY2krq4lhCRo/q9dw9B7LY7hcI4Z1banTll3ndQ0dyQvdyifdu3cbD00BAcN8L\n/jvS3oPZ7KtpoCkqCr/rVaT7+bJ+1cBmp61NTfz5wXw64h6+f81iYempo7zyfN+LYGiaxhfbdnKq\n1YhZUYjRrLy7YZ3dM3UAi9nM5i++JvfsBdr1evwjI4jy8+VH6SmMGTe2x9domkZebgHl9Q2EeLiz\nKmPJoO/5Hk4k21mIZ1BYYCDW+nr0j2TtKuXlTJk8ye5rNTU08KvN31KTugSdry9ZRSW8MjKQ1OSe\nZ0qrli3Fv7CIY4V56BWF+OhIFi5aYHPO3dt3+M3eA9wYG4abycSUhjp+uvFl3Hto2OEI9+5Uc/jo\ncSLDxzF7XuezzNrqu+xoaUdNScEDMI0fz96zZ5lXeYWImOh+38s3IIBEnUbO1avooqJQjUZCsw+y\nbtMrdl1n59795EyYgu7B3+k5q5XffrOd99+0f9+8u4cHc+bOojA8Ap/JU7ACFcBvvtvLL9/5UY87\nCBRFIW1JCmndfiPsJcFXiGEqKSWRwt99xNXkVHQBAah37zLn+hUmLnl6sZTHfbs/m9rVa9E/+EC2\nxsaxJyeb5MTeS44mJSWQ9IRr/uFgLreXr0QPaECp2cwX23bxhhP2aO7af5A9bWY6FixCu3mTSb//\niD97+0cUFh/FujDWNmF21iyKjxYNKPgCvP78OmaeOs2pksMEGjzIfPN1u7sDnW9qQTf7kS9Tej2V\nOn2/++0WX65EW2z7Bepm9ASuVVQSOVEe6TiSBF8hhimdTsdfvvsWubkF3LpUysTQYBa/sbFf16pV\nlG4f7o0BAbQ2NuJvZ3vB793QPVb32MODa4P8ECw7t4AT1TUALBo7itSUJNpaW9nf0II1MQkFUMLD\nuTRiBAcOZhM+ZjTqnTvoH2kmYW1oYPSIwEEZz6y5c5g1d06/X9/TB7bbAJ4cKo+UuvyezmzG4KTV\nB1c6UnKMPZcqaFD0hGpWXlw4l6lT7ctyHwgJvkIMY3q9nqVLB75IGApcVlWbDObApiZ8HylwYC8f\nTcP8+DF18Lbe7DmQxfYRIZA0FYDyG9cxHsxhdIA/rZMm4/7IuXo/P27cb2PF8mVE/+4jrgYtQ+fp\niWoyEVaYT7IDmwm0tbayZe8BqjWNIE1lw9IlBIb0/Px7wagQTm/bhse8eXhERHRurTG497vITMb8\nuZw6fgzLgs6CN5rVSvT1KsYsH94Ly3dv3+GTG3foWNK55fEG8PuD+/l1dBQe/diJ0R+yz1cI8VQv\nLF/KqN27sDY0oFmtuBcdYnVMxFObbTxJYmgQXL/W9bP7qZNkTJ86GMMF4EhNHYx9WMuc8PEcuXuP\nmIkxeF+5YnOuajQy2mBAURT+4u1NrCk7x9wjRaw4d4q/envTgN7nk2iaxj98+iUFsfGUJyRzJCGF\nX327o8eCJHkFh9h2tw7vZcvQWpqx/Pb/sazsLG++9Hy/7x8ZE817UeFMKshjTGE+iw4X8vPXXxrI\nW/pByD5cgiXWtupZc0ISBXmFThuDzHzFoDpaXMz1qjOgWPH0HMPKtc857INLOI9vQAB/+ydvU5h/\niPrKyyxZmjzg7OS1y5cRWlzC8aJC3NBInzOTif1IBuuNuYfZoFlR8A8MJMVdIausDKZOxVpfz/hD\nBaz8cWezDHcPD55bs3LQxvEkR4tLuBUXj+7BtiRFUahLS+dgTp7N/l+zycT267cxpaahAwwzZmIN\nH0/JV1+yNjOjX9nO35s9eyazZ898+onDiIebHjo6wP3h+odmNDq8UcujJPiKQXPsyBEspuMkJnc+\nH2ttqWfbN1+w4eX+PWcUQ4tOpyMlLXlQrxm3OJa47ruPBkW0AtU3b9JeWoqiKHjOmkXMgyISL69b\nzfxL5Rw/epgxQYEk/ck7XL9axeHTZxnh7UVGeppTsq5r6xtQpo+3OaZ4edFibLc5duXSZRomTLTd\nJxwQwK2oGD7YupP/uOlVh491OFmxJIVDW3dzf2kG0LkCMaq4iLh333LaGCT4ikFz7erZrsAL4Ovn\nSYf5Vr8zMYUYiBkR4RSVl+OXkQGqinnvXhYnxnb9fsLkiUyYPBGA3Qey2NmhoMUmoN6/T8FHn/Ff\nX9mA/xOeabc2NaEoCj7+/S9vmpqcwHe79mNKSe06pj91kuQF82zOGzc+HO+sAjrCwrqOaRYLiqJQ\nIU8P7ebj78/PkhezPT+HBp2eUNXKKxvWOXWVToKvGDSK0j1zUlE0VFUdMv01xbPj4JVrGNLSO3/Q\n6zGsWcO+glymz7Tt12sxm8mqqUdL7Uwy0vn4ULtyNdsO5PBGD89T7zc3869btnMlIAhFU5nY3MRP\nX33hqcUmSs9fIP/CRQCSpk5ixqyZ+Pj7s3FCJNuyDlIfGEhAUxOZ4WMZO962jaXfiBEkKBoHKyvx\niIlBbW+nec8e/FeswL3kSH//iJ5pkdFR/Cw6ymX3l+ArBo2ffzgN9bcIDOpsH6eqKh3WAAm8wm4m\no5HffbONcnToNZhlcOPNl563a2bSqHQ/t76HWWJDzT2aR47k0X+lik5HfbczO324Yw8VSzM7+/YC\nFzs6+GT7bt599cVex1JUXMJnzUasCZ3L9qfLStl4+AhJ8XHExS5k0cL5tDY24hPQ+/+XjRvWYf34\nM747eRJGjsR/9Wpoa2Oej317hcXQIOsVYtBkrFhJ+eUA8nLukJ97k7ycNtasl+e9wn7/tmU7Z+KT\nqPUwUK1pHDB18OW32/v02tyCQ7z3y19z82oVzQcO0JKXh6ZpaJrGaK37VqbgMaMJqq62OaZZLIzR\n9/zxeF3R2zaNcHOjSn3yXtvsyiqs0x/OuNWp08i58jDTW6fT4R8U9NQvqm+8uYk/XTSXuXqIOXqE\ntdev8NoPpJGAsCUzXzFoFEVh7YaXXT0MMcSZTSY++XYHlaqGh6YRNzqUlRm2jTfKVYWmXbvwX74c\nvZ8f1qYm9n3xOa+9sP6J+QMXL5TywclzeKxYQeCYMQBY6utp3rWLCTqFV9d3r2mt1+tZNzGKzQUF\nmOLj0WpqiDh5nPW9lGz00lSaHj/2lPd8v4cxt/az8WxSwmKSeilSdr+lhdy8Qny9vUlKTeoK5m2t\nrZSdLyVmQgwjetlDLJxLgq8Qot80TSM7O49L9Y14o7EmJZGQB+36emIxm/mb//N/qXnpFZQH2cRb\nb97AO7+Q1JSHxSiNN67hu3I1er/OwvT6gACsK1dy+sQp5j6WjPSoQ6UXsfr54f4g8AK4BwXh29bG\n//z5n/ZeCjM+jrnTm8gvKGLMqFDmvvd2r0E+cexotly9ClGdzwuV8sukRIb3eO73wlQrdY8kHmqa\nRngPs/CBOHXqDB+dv4gxIRHNaGTfB5/w5xvWUnL6DHvrGrk/ZRqG/GIS6WDjhr43cxCOIcFXCNFv\nH27eQvHUGeimzEDTNM7uy+YvM5cQ2kPHo7LSMn5ffIzbo8bg/8g2HiUsnKOF+aQ+cm4wcC/UNoi7\nj4/g6omSJwZfvQaa2j3xr02vx2IyPTEpyjcggFV92N+bmZ6K3+EjHCsqQAHio6NYsLD3MQG8sWYF\n/7JlB1fHhqEpClG3bvDGhsFdLt5+roz2JemdJTM9PKhdtZpPtu6kPCiYjsRk3ABrSAh5lZXMPXuO\n6bOerb29Q40EXyFEv7Q2NXHCzQNdSAjQ+dihZUk6u/MLebuH5ghfHz9Ny9JlKLm5T732Wy+s5+/O\nnsV91qyuY7ozp4mb9+S6yBmxC9j1j/9CR0ICbg+2AKkmE1ZPT3Jy81mxcrk9b7FX8fFxxD/9tC7+\nI0bw3378Bvdu3UbTNEJX9L+/dUFhEUdv3gYgbnwYiQmL0TSNmsdm9YqiUHHzFpblK20WuJWYGE6V\nHJbg62ISfIUQ/dJYW4cxKNimRrKiKNzv5VlmtaJD0etR29rQzOauZWft5g0WjrGdKU+cMonMsovk\nHz2KefJkDGWlpBvcum3BeVxYxHgypk0h99AhFL0eFAXNYsEvNRWqygfydgfFyF765PbV3oM5bPMN\ngKRUAC5VVWHMySdjSQrBqpW7j50/OiCA61VVEBPTdUxtbmaUn++AxiEGTrKdhRD9MjYqklHXqmyO\nqQ0NTBzRcwPxQDozgv0yM2nJzqbl4EGsX3/F8y0NpKV2bz746vq1/H1SLD+pq+Yf0hJ44bElYZPR\nSHNDQ7fXvbHpFcZ4uOGfmYn/smUErFrFiOIilqSl9O+N9qKpro69u/dy8ULZoF73SYrv3oPwRypi\nRUZSdLsz5K6ePAH3okNoqopqNOL/3R7efe1FplVcRm3qTBFTjUasX3zBzlvV/PzTzXzy9Va0AXRF\nEv2naE76k68zVTrjNkIMGe3tJgpysjFbTCxOSCE4pH+t9+xR1mFE5x7h8Pt879y5C3x29CR3o2Pw\nqq9jvsnIj199scdkpYKiYr6obaRj3nywWvHJy+HnKYlERNk3XlVV+eDLbzird8dk8GR8Qx0/yUxn\nzCOzysuXLrPt2CnqdTpCVJUNcQuInhDzhKvaZ19WLjsamrEsioWqKqaWX+Jnb210+J72X3z+NQ0p\nth2HQvJz+dWDZgj192rJPnQYb4MHS5ekYvD0RFVVsrJyudbSStnpszRu+hF67wd78ZubWV5+kRfX\nrXLouJ9V8R49fxEFCb5COMTtm7fJ2vcpyWmjcHfXU3K4mqgJS5i7YIFD7+vs4AudwfBW5RUCQoLx\nD3xy39tb166Te/wkBr2eFWnJ+AYE2H2/rTv3sGfiVHR+Dz/Yxh/Yx39/e5Pd1+oP4/37/GLnPtqT\nHta5tjY1EbFnJ82jx2JRFKI1Kz9ZvwYfv94/fPvjN59t5mRSCopb5xNDzWJhUfEh3n2tb52I/tOn\nm2lbkm5zLKwgj79+rfcCIaL/nhR85ZmvEA5QVLiPZSse1uGNTxpLfk6Rw4OvK+h0OsInTujTueMi\nxrMxYvzTT3yC8vtGm8ALcNPHF5PR+NQSj4Ph0oUyWh7rB2w8fZqq5atwC+pc3ThvtfL7b3fyfi97\nhfvrnRfWYfpqK+UGT9BgkqWdN3pIbutNTx/47j0cE44nwVcIB9ApbYBtVxxFMbpmMMOMVw/5XJ5m\nM3p354SRqJgovPIO0zHqYZKYajR2BV4ARa+nUu826E1FDF5evP/m6139fu1t/D7Hy0BeUxO671cc\nqqpYPG7Mk18kHEISroRwAE3r3l9V1bxdMJLhJ2PWdDxOnuj6WautZYGXATc358wlAoKDSVRU1IoK\nAKwtLXhW3+l2npuqOqybl4fBYHfgBdj4wnOsqrzM+II8ogvyeN1qIi0l0QEjFE8jz3yFcIC71TXs\n3fERiSkheBrcOFx0l6kzMpk158n7VAfKFc98XeFi2UWyz1zApMD0wACWZaQ7vW3lhbPnOFV+hZF+\nPnh6evK55ob2YEuP2tJCwpmTvP3KC04dkxhaJOFKCBfo6OjgUF4B7e1tJKam4evbfTY82J6V4DsU\nFR46TNH1W5iBqd5ebFi70qn9YcXQI8FXiGeEBF8hhg7JdhZCCCF60GGxsGdfFrfa2xnppmft8gyn\nZM1L8BVCCPFM0jSN//XRZ1SkpaPz9kY1mTj38ef89btvObxgijyQEEKIQdBhsdBQcw+1h65KYmg6\nc/IUFbPnontQ8UtnMHArOZW83AKH31tmvkIIMUC7D2SRfbee5sBAQmrvsWH6ZBYtnO/qYYmnuH7r\nDsoc28I3uoAAai5dcPi9ZeYrhBADUHGpnJ240ZqWhm7OHOqXZvDHsnLa29pcPTTxFImLY3E7WmJ7\nsPQCi2ZMd/i9JfgKIcQAFJ+7gDbN9sP6/uJ4DhUUuWhEoq+CRobw3Ag/vPLzMd+4gcfhIjJNbcRM\n6lu51IGQZWchhBgAXw93VJMJ3aMVp+7eZfToUNcNSvTZ8qVppBmNVFVUErYifdCbYfRGgq8QYtB0\nWCx8sPlbLqqd5QOmuel45+UNTin9WH39Bl4+3gQEB/f7GpWXKzh3oYw5M6cR2ccWhCuWplH0x69p\nXLESRVHQLBaiLpxjxntv93scwrkMXl5MnjnDqfeUIhtCDCOuLrLxb19+Q/GixV2zQLW9naQTR3nT\njs479rp5/Qa/O5jLzbBw3NuMTG2s46cbX8HNzkYLv//8K46NGgtTpkDpBeLra3mrj+Uh6+7Vsj0n\nnwYUxup1bFi9HIOnZ3/ejhhGpMiGEMIpKqyqzfKrztOTS5aOQb+Pqb2dsydPMy5sHB/nFFCduQI3\nQAPOm0x8vWMPr73wXJ+vV3ruPEfDI1CiH8x2p02n6OJFki6VM2HyxKe+PnhkCO848AuGGH4k+Aoh\nBk1PHyiD/SFTVFzC1xVVNM+ajf5MGe2WDh6tR6QzGLhqZ8A/X3EFZeFim2PKlCmcOVbcp+ArhL0k\n21kIMWgWBI5Aq67u+lm7fZvYkEC7rnH7xk02b93Brt3fYTLa9kC2mM1suXyVtrQluAUHw5w5mOne\nzcgH+56mTQwLQ71xw+aYdvUKUydE23UdIfpKgq8QYtA8tyqTDfU1RBbkEVWQx4vN9axevqzPr88t\nOMQvT5wla+Fitk+ZwV999hX37tZ0/f7KpcvUT3w4E1UUBb23N5aqqq5jhmNHyZwz065xz1kwl5ml\n51Fv3wZAvXmTOVcqmObkJJyBaLhXS0tjo6uHIfpIEq6EGEZcnXA1EJqm8V8++4r6Jek2xxccyue9\nB4lPzQ0N/CLnENbYuIev6+gg5pvN+I0fjzuQMX8O0X3MVH78/sdLjlNx5w6Tw8KYt3DegN6PszTU\n1vHPW7ZzZWQoqsmMZ1kpf/bKBqZOn+bqoT3zJOFKCDHkmYxGGnvoJlP/yLKyf2AgcVYLhbduoRs3\nDs1sJujgfn767tv4BgQM6P6KorAwbiELgYsXSvly6w7CQoJJTEpAUbovbQ8VH+7Zz81VazA8GKO2\neDF/+/HH/Ou/DyF4lOw1Hqpk2VkIMSQYvLwIbrtvc0xTVUY9FvfefOl53lVNxB4pIvPsSf7HxpcH\nHHgf9ek32/jHmkZyFsXzsX8wf//bD7FarYN2/cFWabHafDlQ3N2xRkTwXeFhF45KPI3MfIUQQ4Ki\nKKyfPoVPc3MxJiSgNjUxrvgwL73+YrdzYxfHEuuAMdTcqabI4I3y4LmyLiSEKylpZGXnkrlsqQPu\nOHBuZhPdcrs1DYtzniiKfpLgK4QYMhYumMeMqZPJyysgcMQIYv/dO05d8i0tLaNj8mSbJUGdvz+3\nm1udNgZ7LYuOYEtlJR4xnc+5jWfP4mE2kzTLEV9PxGCR4CuEGFK8fHxYsWqFS+49e/YsvsovpiP2\nYeCy1tURHRzkkvH0xZrlGVi/3c6uokO0WVVCPD1Yv2iB7E8e4iTbWYhh5Iec7TxUbNm1lwOaHnXu\nXLSqKqZfvsjP3tqITicpMsI+T8p2luArxDAiwXdw3Ll5i5JjJ5kYE8n0WfbtGRbie7LVSAgh7DAm\nbBzPhY1z9TDEMCbrKEIIIYSTycxXCCGEw1mtVgryCmluvU9acjz+gfbV/B5uJPgKIYRwqMa6On79\nzXZqklNRvL058F0OmyZEELdogauH5jKy7CyEEMKhvj6Qw71Va9D5+6O4uWFOTmZn2WWclO87JEnw\nFUII4VB1On23Yil1nl5YzGYXjcj1ZNlZCDGstLe18cn23VxHwVvTSJ8QRVzsQlcP65kWrFqp1DSb\nABxkasfdw8OFo3ItmfkKIYaVf/7yG44lJFOTnEpVShofN97n/Lnzrh7WM23D0jSC9+zG2tqKpqq4\nFx9m9cToId0tytFk5iuEGDYaa+soDx6Jotd3HbNOn07+oQJmzJzhwpE924JHhoEZA74AAAJ3SURB\nVPB372wiOyePljYjaSnxBIeOdPWwXEqCrxBi2FCtVjS9G4/Pp7Rnd4I1ZLi5u5OZmeHqYQwZsuws\nhBg2gkaFEnn3jk0WrVJZyeLoKBeOSojuZOYrhBhWfrp+NX/Ys58bOje8NZWUcWOYvzDO1cMSwoY0\nVhBiGJHGCkIMHU9qrCDLzkIIIYSTSfAVQgghnEyCrxBCCOFkEnyFEEIIJ5PgK4QQQjiZBF8hhBii\nbl+7zrkTp7Bara4eihhkss9XCCGGmA6LhX/69Asujg2nIziYkE++5K1F85g+Y5qrhyYGicx8hRBi\niPl2917KUtNRZs7EfexYmpZl8sXxU890/9vhRoKvEEIMMdfNVnQGg82xuyMCaa6vd9GIxGCT4CuE\nEEOMv6Z2O+bb2oqPv78LRiMcQYKvEEIMMWsSF+OTnYWmdgZh7epVkkb44ebu7uKRicEitZ2FGEak\ntvPwUX+vlj15hRg1jfmREcxfNN/VQxJ2elJtZwm+QgwjEnyFGDqksYIQQggxhEjwFUIIIZxMgq8Q\nQgjhZBJ8hRBCCCeT4CuEEEI4mQRfIYQQwskk+AohhBBOJsFXCCGEcDIJvkIIIYSTSfAVQgghnMxp\n5SWFEEII0UlmvkIIIYSTSfAVQgghnEyCrxBCCOFkEnyFEEIIJ5PgK4QQQjiZBF8hhBDCyST4CiGE\nEE4mwVcIIYRwMgm+QgghhJNJ8BVCCCGcTIKvEEII4WQSfIUQQggnk+ArhBBCOJkEXyGEEMLJJPgK\nIYQQTibBVwghhHAyCb5CCCGEk0nwFUIIIZxMgq8QQgjhZBJ8hRBCCCeT4CuEEEI4mQRfIYQQwsn+\nP8ctGsamB3K3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118cfeb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "visualize_classifier(model, X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regression\n",
    "\n",
    "In the previous section we considered random forests within the context of classification.\n",
    "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n",
    "\n",
    "Consider the following data, drawn from the combination of a fast and slow oscillation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7\nwTRSlanVbclEryzjPGu9tck6BWseizipMer8BlWJbvB139pQFvWuX6IbpoUL5s3ZBlTFvZJD3e0p\nyCzX6t1m7FvBXbWiD9P52ZYu2oaBObs8ybxbldtaPT8KiciCx1LGOU9/2W/w+3oyyHSktCy6Ub1y\nRJYb2nq1FUQ3TKdnS66/pwJPAdk0Tdxzzz3YuHEjbr75Zrzzzjt+t6sl1cF53cqlePbQUc8XbavX\nUjqTZV0qmdIvnXEb0tGp7m8jxzI0uEqJQvOtnBe3OU8ZLrAqsm8nqWMwnjk9O2fliCw3tPXWGjsl\nsl61om9O79jp91TgKSA/88wzOH36NPbv349t27Zh586dfrfLN61c6Oy9FgBKDAW6rdXTaWE9j6XM\nbatJGS6wFCxr/XWzTs04j6BEfXPu9n22lpvZt/IVJXoB8ub5OPEUkA8dOoTVq1cDAD7+8Y/jl7/8\npa+N8lMrFzpVhwLd1hXrtLCex1JWL6kx6gsstS6Itc+iKl1R39B6mcN3W/oqY56PiKcshVwuh2w2\ne/ZF2tpQKpWQSLjH92SyfBvX05N1fZ5X1a9v/fd5v5fFW8cna577kd5s3Xa8e0I8FBjUMXiRTBrl\nSW6Uj/26P8yis7Md3/7B/8Vs0cSyczqx4aqP4opL+9DZ2Y6hfYdqXuOGqy/Eoz8aAQA8ctdnQm2/\nVzdc/THhsVjn56XXjmLiVB7Foolk0sCC9raWz10Q576RYxGxzrfT7wPlC6xM39dGNdtmv68vTtcT\n+3+HRfSeL712tFIU5L5Hf175O29FI9fGINmvX0D58ja/vRyuKufAMJBMGujpyQqv80u6OnDdH14Q\navtb4SkgZzIZnDp19i6qkWAMnL0jGxub8vK2Tb2+9d9Xf/IjjoXnr/7kR+q249zF4vXNT714RJo5\n5GLRhLXOxTqmi/oWYuGCctGSu2/5ROVnF/UtxJbrL8bep0Yqdb9vva4fF/UtDPz8+M1+LFYN44v6\nFmJsbKp2Z5iiiclThZbOXU9PNpDPp96xNPL7yYThuFb2nMULlDmnFi+f8/1bVgLw7/tr/T089eIR\nnHh/BsWSidvufwYfzMwinUqG+plW/21aPeX1a86f8/1+6/gkhvYdwuTkTN3vt9s0RiPXxqBVX7+s\ninM113fTRLFoYmxsSnid//wV/yPSY2n2xsbTkPVll12GF198EQDw+uuvY/ny5V5eJhROVajs2dGi\n4SC3oUCNLKjUAAAauUlEQVSVhwGthBVryzZZbiy8sCffVB+LaglsbscC1B+2FA3pqTRkJ5t8oeha\nRjNKXr/f9htVy6JOdbdbbeQ6rwJPAXnt2rWYN28eNm7ciPvvvx9f+cpX/G6Xr7wGoIH+XmskuEbU\n8yxUn05JX41Ip5JzluapelGSiShfJDddiHyZkNfvt3C71bSc5XIbrdWtQ0fD05C1YRi49957/W5L\nJLbvfhXjUzPCpTEfXrLAcdhalsSh6mVZ89tTLW1rqUvpQ4uopKos5y4I1vIcazmLdfFV8eIkA7dy\nmdYyISCaz7crMw8np/I1jy/MzHP9PR1uVIcGV0m1wYdfIi2dqQKZd0KyL8t66/hk7Ja5WDdUTmQ+\nd0HJF4rKVyuSSSPlMqWbAqnTm3RbVkTRYkCuw74IHYA0OyG1Mkc6PjWj/ZZtThWXdB/CFQ2xShc0\nfBDGNoiNlMuMqmc5kTvt+Pj7p5wft9RbViTb9pJxolVx1qCGXK3dlKr/LQMdhp6CYF1MhgZXzdnF\nyutOWCoRDbHG/TvhVTqVxI1rl1ey351ENQXidUrGuiG1htuTCQOlkinNdS3OQu0hDw2uCm2eMqj3\nCntLOzc6FcYgf4iGWPmd8M5KFrKPlFmimgJpZUpmoL8XiYRRSYASJa9SuLTqIbdifGoG23e/Kk2w\nbcS6lcscly+4/UFyKEpvHek2x5q+Ks+bV494hMn+fvbesrVWPKopEHtPN+r2BKF6edlELj9nCqGR\n70NU3x2vOIfcIPNM6r1Mu6LY50iXndOp/RwpubOWPlniMG8epnprxaNoj1XLWob2+MmeoFgsmchN\nF6S49gaFPeQG5AvFOYmLUS93qFY9R/r3t1/ZcFUaawN76wZj3cplkR9Ls9wyrOPMynnozrbHYt6c\n9GL1akUJit/70a8ARH/tDUJsAnK+UJzTw20mALllrsr8pRAN08h8g0FEc1m1BoolE3c/Mox8odhy\nAtbwyChKZ5LUJnL5yg26TEqCJLpCseR6vZIpz6dZsRiyHh4ZbWltpm6Zq3FaGhMnYSZNUjjstQas\n0p2tTJ3V1HgvmVJuK9uWdM800/F6FYsecqs1jd2K9qtItxsMle+I7UTHYe8lqTjFQM0TXbuAsx0L\nA2gqS1qVbWVn6xRKUPV65SYWPWS39brWUHbJLA/dON1x3npdv+Pvq5q5yqUxanHqJelSfYtFKNy5\n7fNraXa0WfSabmVCo/DhJe7XI6frleqjRLEIyKL1ugsXzKvJ4nO60NmrdameucpdgdTS7AjP0OAq\nYW12GVlLDqlWEGUuRa/ZSJnQMLnttlf+uX7Xq1gEZNGJPT1bcnzc6UKXTiWRMCDNcodW6LI0xp6o\np0OP0QkrssVXvaDk52s2UiZUBipvE1lPLAKyU03jq1b0ORZQAJwvdKr1OtzkC8XKfFEyYShZTKCZ\nRD3Vh7FYkS2+7NcuJ5mOVFPXJus1LYs6y5W6ctMF3L7rFYxPzUhxk6vaNpF+iEVABmoX9L/x9rjw\nuTpf6KzF9tZ8kWiYXnaiP9Ynnj+CiVxeiguKX+K4a5WdfVjbSnIL6zxHeVNXfe1yGtnysgRqoL8X\nH+ruQKYjhZOTZ5c9nZzKVzadiTpXIY4jQ7EJyHZuyRI6X+h0WfIkOn8np/LaJT/FadeqRpK8dE5y\nq8fvqbNGMqujujbEcWQotgFZdLIXZdNaXuiA8oVMlyVPzSS7qHaz4US2ko1hsXrC1asgWl3G2CrZ\npkBaaU8jmdVRXRviODIU24Dsliyh4522vRiAnWp3nc0ku6h2s+EH2YKGF/aesDW9csxhy0Egnue5\nVY1kVkd1bbCvbkkmjKb2ordPa3zxuy9Ln82vRlpdAKyT+tC/Hp5TNu7kVF65MpKNXHjdCgwA6t11\nDvT34vGnf1NJ7OrryeCDmQJOTuVrnqvazQaVib6zbckECsXaFRI8z80T7Q5WLcxrg70ATrWuTLqp\n16nugDjtGy2j2PaQAWunFOc7RB2GOau5zZmrOB85PDJakym+4coLHJ+r2s0GlYkLWDgvV+R5bp59\nCeSibBpWpzmZMEK9NojKhHqhSjUyu1gHZEC/MpIibsUAVAzGTkOZAGKT/BQHou/suUsyPM8+qk4U\n++Zffgrd2XYkjHKPNMzPtN4oXjNUqUZmF/uAHJcykqoXA6jmltQT1+QnHbkl9fA866demdBmNr9Q\npRqZXewDclzKSNqXziQTBgyg5W3cohDH9Ylx5PSdZU9YX/VWTuSmCw0HZVU7ILEPyOlUEtX3TG7D\nX6pnrlb3Kroy6aZ2iJFJHNcn6mh4ZBTjUzOV8qe373ql5oJrfWejGEKlcDWycqLROWCntfuZjpT0\nHZDYB2SgvHWZLnWq4yCO6xN1Y+UBlGwrHJrpBVFwSma5OlqY7CU9/Xi96mkN2YMxwIAca6ruI8yh\nTPW5JfDIngkbhaHBVVi/5vyaIim6Ka98Ef9c91EwuQfUfaZi8CFnA/29OPDC7zA+NcOhTAW5JfDI\nnglbj1V8ws/rjX1dbfXKAq/f/Ufu+gzGxqakLJZhwHmfZ91HwdhDJqLQuSXw2DNh47LNppuoy4WG\nzTAQy1EwBmSUh2512VqR9KV6UmE1twSeYsnE//nm8xgeGW1qm02dhbmywJrDL5mI9AYojgl9DMhE\nFDorD0A0X/jW8UnsOXgYTzx/xPHnuvYMRcJaWSDjDZAfuS5hb9fpVewDsr3X0cj2b0TUuoH+3kpV\nKFHBBqfa5ED81pwHtbLAvpuWjjdA+UJRme06Y5XURWfpMvSpaqY4zdVsItc5ixfEKhvbGq7d+9QI\niiUTfT2ZSsUyr1567WhNopiON0Bue8DLNgzOgEzaYYBWTzJhOAblRZ1pnJysDRKtBiMVWSsLAOC+\nzZe3/HpPPPvbhp+r8nIjlfYriP2QNRFFT1TScMOaC+bsRqTSRhKyT3+9PTrV8HNVXm6k0n4FDMhE\nFLl0KjmnStOyczorgbd6NyJW0vPPeb1Zx8cXZc/uO6zSDZCISvsVMCCfodOSEjdxOU5Sj1WlKWEA\nf3/7lRjo7630MFVZlrh996uhl5z0asNVH3V+/MoLKuch7Bug4ZFRlMyzS65aLaM6NLgKD25d7VjX\n2hr+lwkDMhFRDF1xaZ/jvtIA5gTFsLKR7dXIjo6d8q22uaiutWzTCkzqImWxp08yaaZ3HER5TS/s\niWJOQbHVEp1uqj8HUTWyOGXTs4dMRFIpmcDmr/0k6mZ4Yp7pWZ6YnMFELq/czlVRlugUVSNTvbZ5\nMxiQiYh8kC8U52yIUCyZyE0XpCxAIRJmiU47UTUyUZa0jhiQiYh84FaAQhVhleh0IqpGdut1/YG/\ntywYkIkoMkODq5TJoK5HpQIUIkGV6GyEfZ9zHZZcNYtJXVWsuq7Fkom7HxnGupXLYvVlIIpC1IlN\nfhFVG5OxAIWIdb2zErn8KNHZ7Pv7WY1MNS31kJ9++mls27bNr7ZEysouVKEAORHJR6UCFG6q14Oz\nEEu4PPeQv/71r+OVV17BRRdd5Gd7IuOWXcgvJBHVk04lcWq6UEnsSiYMdKTbfL1+6DKaQM48B+TL\nLrsMa9euxT/90z/52Z7IRJldSETuVAlEhgEYUKeyWLNkWT/t1fjUjFSFQOzqBuQDBw7g0UcfnfPY\nzp07ce211+JnP/tZU2/W0+NcO1UG5/1eFm8dn6x5/CO9WanbbadSW1XFzzg4S7o68N775eIaPT1Z\nJJNG5b9ll0wa5YgMoFAs4tTMLIpFE/c9+nNsuOqjuOLSvjnPfe/9GXx5z0/xyF2fiarJ4s/YcP7c\n/T4fTq8X1Dm3zo/1+tVk+X7VDcjr16/H+vXrfXmzsbHGdxcJ29Wf/MicCjXVj8vc7mo9PVll2qoq\nfsbBGR4ZxYn3Z1AqmTgxOYOnXjyCYrE8AKzCZ/7BzCxKZ3JQJk8VKo+/dXwSQ/sOYXJypjJ8XSya\ngGmiWDQjOzbru+z0GXdn0jWPAfD9fDi9XlDn3PrMP5iZxXR+FsWSWZlWCOocNBvouezpDKbcE0XH\nnlRZLJrYc/AwxqdmcGJyJtSayl4Mj4wiN11wfY6s65HjsuHM0OAqzG9PITddOPs9O1O85YvffTni\n1pUxIFexFyBnMCYKhyip0lpFJPuqhyeeP1L3OVY+irW8smQCE7m8tMfkN/tGDtbnEOYNl6h4iywV\n1Vpah3z55Zfj8svjt1aMiPwlSqq0k3HVw/DIKE5O5es+75zFC2o2byiWzEA3b5CV2yYWQXKriy3D\neWAPmYgiJyrZaCfjqgdR795u3cqlkW7eIJOoPod6dbGjPg8MyEQUOVHJRjsZq17V691X56NweWVZ\nVJ+DqHhLWO9fD0tnElHkBvp78dC/HoZZZ6c9GatenbtkPo6O1V7IE0Z5PXJ1CUjRc2W80QiS2+cQ\nZMnMdCoJAMIEPC/nwc+12ewhE5EUPrxEfDGUedWDqHc/vz3V8HNlu9EIOvM6ys8hnUoi01F7bsJ6\nfzcMyEQkBdFFOtORknrVg33JJFBus9Ubc3tuMmFIe6MRpKiXmVpBWbbzwIBMRFIY6O+d03Pp68kI\nA5tsrCWT1qYMbm2ufm5XJh15EIhK1MtM06mkdOeBAZmIpJFOJZEwgA91d+C+zZcrEYzturPtWhba\n+OJ3X8aJSTUKtTipXvc8kcsjXyhG3aQaTOoiIiJX9kpk1euGZehZ1uO0/rteZbUosIdMRBSyfKGI\nkgllepuqr58WtV9UuSsq7CHb6DjURETyULG3qfr6aVH7rcpd1o3RupXLWKmLiCguVOxtiiqpqbJ+\nupFKcDLUS2dAJiIKkYq9zVbXDUexkUS1RivBAdHeGDEgExGFSMXeptOStEbX7dq31oyiJ+q0/lsk\nyhsjziETEQVAlI+ybuUyx52Noq4SVU86lcQHM4WacqD1yDJEP9DfiwMv/K7y74lc3nH3J6cbIz/L\nY7phD5mIpDE0uArd2faom+FZI2uQq4MCIHdZUD/IOEQ/NLgKt17X7/izZm6M/B6KZw+ZiKSl+6oH\nq0qVztw2kohy2dFAfy8ef/o3lYz3vp4M1q1c2vCNkduezl5vrhiQiYhCVmdbXq24DdFHPSrw4NbV\nleHoZm+M3IbiGZCJiCKke2/eq1Z7orIKYiieAZmIpDI0uAo9PVmMjU1F3RQ6w5orLZnlZKjhkdGm\nAqrXhDCZBbG3NZO6iIhCVjLL/1OBfdlSsWRiz8HD+OJ3X/b8mtt3v1oZKpaZWzuD2NOZPWQiIhJS\npQ502KwRgr1PjaBYMn0ZimdAJiIioXp1oOOsem2zH0PxHLImIiIhUWUxt2pX5A0DMhERCYnmSjvS\njQ2wbt/9KsanZnxskb44ZE1EFKJ8oVj5by8Zy2Gzz5UmEwY60m1Ip5IRt6x5si9NYw+ZiCgk9r2Q\nrYzlKLf8a8RAfy+6Mmks7mzHw3dc2VQwzheKlaxy6wZEJflCMbSdqhiQiYhCIstGC2ER3YBUjxLI\nLF8oIjddCG2nKgZkIqKQHHMoJAHIvRdyK1RfMiVqZ1A3UJxDJiIKwfDIKEQLhWTeC7kVKiyZcptX\nFrUzqBso9pCJiEIg6i0C8u+F7JXqS6ZE7QzqBooBmYgoBKLeYsLwvl2f7FpdMhU1UTuDuoFiQCYi\nCoGot9iVTYfcktZYG000knU80N+LTEeq8u++ngy2XH+xMkum0qkkMh2pSk/Zan9QN1AMyEREIRD1\nFk9OqrMUKF8oztloopGs43QqiYQBLO48u9NTWMuIvNq++1V88bsvYyKXr2SJZzpSuG/z5YGOZjAg\nExGFYKC/F4sEvWFVlj21mnVs3zkq6GVEXtmXOxVLJnLTBcd2Dg2u8q3gCAMyEVFIJnKnHR9XZdlT\nq1nHqqzDDnu5k4UBmYgoJKJ5ZFWWPbWadSxKbJPthiTs5U4WBmQiopAEsal9WIYGV+HW6/odfyZq\nv5UAZpXN7MrMc3yebDckYS93sjAgExGFRJR1rMqyp4H+Xmy5/uKGso7t88XFkomTU3nH15XthiTs\n5U4WNRaDERFpIp1KIjddQMLwZ1P7sA309+LAC78D4N5+0Xzxos403s+dRrFkoq8ng3Url0p3Q2It\ny5rOz1Z2uCqVTBx44XeBtpUBmYiIfCeaL34/dxpdmXK2ucw3JOlUcs566TD2dOaQNRER+U71BLYo\nMCATEZHvVE5giwoDMhER+c6eAJZMGEolsFUbn5pBGBtUeQrIuVwOX/jCF7Bp0yZs3LgRr7/+ut/t\nIiLSVsIAurPtUTcjcAP9vejKpJEwgK5MWslgHCZPSV3/+I//iFWrVuHmm2/Gm2++iW3btuHJJ5/0\nu21ERKSB7my7b+Ulg2atnbayqzvSbejOtoeS1OUpIP/5n/855s0rL/CenZ1FOq3WbiVERFEZGlyF\n7btfjboZ5MBaO22xaliHpW5APnDgAB599NE5j+3cuROXXHIJxsbGcMcdd+DOO+8MrIFERERhEK2d\nFtW29lvdgLx+/XqsX7++5vE33ngDt99+O3bs2IFPfOITDb1ZT0+2+RZSU/gZB4+fcTh0/pyTyXKi\nU9TH6PX9m2m/03NlOX67d084r50ulkwkEgaSSSPQNnsasj5y5Aj++q//Gt/5zndw4YUXNvx7Y2NT\nXt6OGtTTk+VnHDB+xuHQ/XMuFsspu1EeYyufcTPtd3quDMfv5NzF83F0rHYDiWTCgGmaKBbNmjZb\n0w9Oc+TNBm9PAflb3/oWTp8+ja9//eswTROdnZ3YtWuXl5ciIoodVRKcRFRvv8i6lcvmzCFbOtJt\n+GAm+LlkTwF59+7dfreDiIhiQtaAbi3L2vvUyJxa248//RuUTODE5AzufmQY61YuC2QJFwuDEBER\nnWGtnV7c2V6ptV2daX107BT2HDyM4ZFR39+bAZmIiEhAlHn9o5/+t+/vxYBMREQkINq16viJ2uSv\nVnH7RSIiCoys88WNOneJc+Z1ELtWsYdMREQkcOF53Y6PB7FrFQMyERGRg+GRUTx76GjN41et6GOW\nNRERUVhECV1vvD0RyPsxIBMRETkIM6ELYEAmIiJydO6S+Y6PB5HQBTAgExEROVq3cpngcf8TugAG\nZCIiIkcD/b3Ycv3FSCbKu1MlEwa2XH9xJaFreGQUE7l8paRmq9W7GJCJiIgErFKaCQPoyqTnBOM9\nBw+jWCrvXOVHSU0WBiEiIqrSSDETt5KaXpdEsYdMRETUpCAysBmQiYiImhREBjYDMhERUZOCyMDm\nHDIREVGTrHnivU+NoFgy0deTwbqVS1sqqcmATERE5MFAfy8OvPA7AMB9my9v+fU4ZE1ERFRHd7Y9\n8K0kGZCJiIgkwIBMREQkAQZkIiIiCTCpi4iIyEXQc8cW9pCJiIgkwIBMREQkAQZkIiIiCTAgExER\nSYABmYiISALMsiYiIvLIzwxs9pCJiIgkwIBMREQkAQZkIiIiCTAgExERSYABmYiISAIMyERERBJg\nQCYiIpIAAzIREZEEGJCJiIgkwIBMREQkAQZkIiIiCTAgExERSYABmYiISAIMyERERBLwtP3i9PQ0\ntm3bhsnJScybNw/3338/PvShD/ndNiIiotjw1EP+53/+Z1xyySXYt28f/viP/xgPP/yw3+0iIiKK\nFU895FtuuQWmaQIA3n33XSxcuNDXRhEREcVN3YB84MABPProo3Me27lzJy655BLccsst+O1vf4vv\nfe97gTWQiIgoDgzT6up69F//9V/YsmULnn76ab/aREREFDue5pAfeugh/PCHPwQAzJ8/H8lk0tdG\nERERxY2nHvKJEyewY8cO5PN5mKaJbdu24dJLLw2ifURERLHQ8pA1ERERtY6FQYiIiCTAgExERCQB\nBmQiIiIJMCATERFJINCAbJom7rnnHmzcuBE333wz3nnnnSDfLpZmZ2dxxx134MYbb8Sf/umf4rnn\nnou6SVo7ceIE1qxZgzfffDPqpmjpoYcewsaNG/H5z38e//Iv/xJ1c7QzOzuLbdu2YePGjbjpppv4\nPQ7AL37xC2zatAkA8Pbbb+PP/uzPcNNNN+Hee++t+7uBBuRnnnkGp0+fxv79+7Ft2zbs3LkzyLeL\npYMHD6K7uxuPP/44Hn74Yfzd3/1d1E3S1uzsLO655x60t7dH3RQt/exnP8Nrr72G/fv347HHHsPx\n48ejbpJ2XnzxRZRKJezfvx+Dg4P49re/HXWTtLJ3717cddddKBQKAMpVLf/mb/4G+/btQ6lUwjPP\nPOP6+4EG5EOHDmH16tUAgI9//OP45S9/GeTbxdK1116LrVu3AgBKpRLa2jyVJ6cGPPDAA7jhhhu4\ns1lA/uM//gPLly/H4OAgbrvtNlx55ZVRN0k7y5YtQ7FYhGmamJqaQiqVirpJWlm6dCl27dpV+ffh\nw4fxiU98AgBwxRVX4Kc//anr7wd69c7lcshms2ffrK0NpVIJiQSnrv3S0dEBoPxZb926FV/60pci\nbpGennzySSxevBif+tSn8A//8A9RN0dL4+PjePfdd7Fnzx688847uO222/Dv//7vUTdLKwsWLMDR\no0dxzTXXYGJiAnv27Im6SVpZu3Ytjh07Vvl3dZmPBQsWYGpqyvX3A42MmUwGp06dqvybwTgYx48f\nxy233ILPfe5z+OxnPxt1c7T05JNP4pVXXsGmTZvw61//Gjt27MCJEyeibpZWurq6sHr1arS1teH3\nf//3kU6ncfLkyaibpZXvf//7WL16NX784x/j4MGD2LFjB06fPh11s7RVHe9OnTqFzs5O9+cH2ZjL\nLrsML774IgDg9ddfx/Lly4N8u1h67733sHnzZmzfvh2f+9znom6Otvbt24fHHnsMjz32GD72sY/h\ngQcewOLFi6NullZWrFiBl19+GQAwOjqKmZkZdHd3R9wqvSxcuBCZTAYAkM1mMTs7i1KpFHGr9NXf\n34///M//BAC89NJLWLFihevzAx2yXrt2LV555RVs3LgRAJjUFYA9e/ZgcnISu3fvxq5du2AYBvbu\n3Yt58+ZF3TRtGYYRdRO0tGbNGvz85z/H+vXrKys0+Fn765ZbbsFXv/pV3HjjjZWMayYpBmfHjh34\n27/9WxQKBZx//vm45pprXJ/PWtZEREQS4IQuERGRBBiQiYiIJMCATEREJAEGZCIiIgkwIBMREUmA\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10babb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(200)\n",
    "\n",
    "def model(x, sigma=0.3):\n",
    "    fast_oscillation = np.sin(5 * x)\n",
    "    slow_oscillation = np.sin(0.5 * x)\n",
    "    noise = sigma * rng.randn(len(x))\n",
    "\n",
    "    return slow_oscillation + fast_oscillation + noise\n",
    "\n",
    "y = model(x)\n",
    "plt.errorbar(x, y, 0.3, fmt='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the random forest regressor, we can find the best fit curve as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118cfe0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor(200)\n",
    "forest.fit(x[:, None], y)\n",
    "\n",
    "xfit = np.linspace(0, 10, 1000)\n",
    "yfit = forest.predict(xfit[:, None])\n",
    "ytrue = model(xfit, sigma=0)\n",
    "\n",
    "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n",
    "plt.plot(xfit, yfit, '-r');\n",
    "plt.plot(xfit, ytrue, '-k', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
    "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Random Forest for Classifying Digits\n",
    "\n",
    "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
    "Let's use that again here to see how the random forest classifier can be used in this context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['target', 'data', 'target_names', 'DESCR', 'images'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remind us what we're looking at, we'll visualize the first few data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hl946Ie3vrVjRTqXjaOSeNH36dC+r8R6f+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c22128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the figure\n",
    "fig = plt.figure(figsize=(6, 6))  # figure size in inches\n",
    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
    "\n",
    "# plot the digits: each image is 8x8 pixels\n",
    "for i in range(64):\n",
    "    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n",
    "    \n",
    "    # label the image with the target value\n",
    "    ax.text(0, 7, str(digits.target[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can quickly classify the digits using a random forest as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
    "                                                random_state=0)\n",
    "model = RandomForestClassifier(n_estimators=1000)\n",
    "model.fit(Xtrain, ytrain)\n",
    "ypred = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a look at the classification report for this classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.97      0.99        38\n",
      "          1       1.00      0.98      0.99        44\n",
      "          2       0.95      1.00      0.98        42\n",
      "          3       0.98      0.96      0.97        46\n",
      "          4       0.97      1.00      0.99        37\n",
      "          5       0.98      0.96      0.97        49\n",
      "          6       1.00      1.00      1.00        52\n",
      "          7       1.00      0.96      0.98        50\n",
      "          8       0.94      0.98      0.96        46\n",
      "          9       0.96      0.98      0.97        46\n",
      "\n",
      "avg / total       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(ypred, ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for good measure, plot the confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IC5iISJBHuidcRbJnXzoWLVuJ4uJiNPD3x2ydFi4uLjaby2zbf68nRoahY9d2\nuJF/EwBw7vcL0E2ei4g5E/B8k4ZQqVQ4eugEYnTxKC4qlmwcSjnecmZbvBRZlPJcinw9Px99ggdj\n7acJ8PXxxkeLl6FAr0fk1EkSjFB8LrMr7nv9OJcir0leigXRS3Hk1xPmx8ZMHAavmp7QTZoLAJj7\nsQ7nz1zA8vjVFvdXnkuRK/rxFpn9sEuRZfsI4tq1a5C66zMys9CkcWP4+ngDAIL790Xq1m2SZorM\nZbbtv9f2DvZo9Hx9DHkvGF+nrsKCZVHQ1KyBnzMPI2Fxovn3nTz+X9T09pJsHEo53nJnS1bAmzZt\nwpIlS3D8+HF06dIFQ4cORZcuXZCRkSFVJC7l5sJL42ne1nh6okCvh16vlyxTZC6zbf+99tQ8g/3p\nvyB+bgIGdBuBo4dO4ONVsdif/gsunM8BANT01mDwsP7Y9v1OScYAKOd4y50t2WfAX375JRITEzF6\n9GgsX74cfn5+yM3NRVhYGFq1aiVJpsn44DNstVram4iKymW2/Nly5/6ZfQnhw7Tm7S8SvsJ74aGo\n6a3BxZxcPPdCA3y0Mhpffr4J+3btl2QMgHKOt9zZkp0BOzg4wMXFBZUrV4avry8AQKPRSLqMpZeX\nBnlXrpi3c/Py4ObqCmdnJ8kyReYy2/bf6/oN66J7347/eNxQbECXnoFYkfgvfBS3Ap+vWC9Jfiml\nHG+5syUr4MDAQIwePRr169fHyJEjsXr1agwfPlzSOyq3atkcR4+dwIXsbABAUvJmBLRrI1me6Fxm\n2/57bTQaMXVmOGp631sAKzikD/578gxeeuV5TJ0ZjlEhk5D2nXQfPZRSyvGWO1vSb0FkZWVh3759\nuH79OqpVq4ZXXnkF7du3f6TXlndB9n0ZmYhfshwGgwG+Pt6IidLBzdW1XPuqCLnMrpjv9eN8C6Jb\n7zcwPGwwVGoVci9exqwp8/HJ+o/g6loZeblXAJUKMJnw68/HMHfmIov7K++C7BX5eIvMfti3IGzq\na2hEFQXviKEcT8XX0IiI6H4sYCIiQVjARESCsICJiARhARMRCcICJiIShAVMRCQIC5iISBAWMBGR\nICxgIiJBWMBERIJwLQgSzlgs3X3MHkbt4CAkV7SuLd8Tkvv93qVCcgGx7zXXgiAiegqxgImIBGEB\nExEJwgImIhKEBUxEJAgLmIhIEBYwEZEgLGAiIkFYwEREgtiLHoC17dmXjkXLVqK4uBgN/P0xW6eF\ni4uLzeZI1FMRAAASq0lEQVQqORsAdHPiUL9eXYQOCpYtU2nHu3WH5pgS+z56twiFSqVCeOQIvNTs\neZhMJmTtOYiEDxMlzS9li++1TZ0BX8/Phy46FvHz47AlaT28a9XEwsXLbDZXydlnz53Hu+ET8OPO\n3bLklVLa8fauXRPvTbpXvADQqXd7+NSpheG9xuO9vhPxUrPn0aZjS0nHYMvvtU0VcEZmFpo0bgxf\nH28AQHD/vkjdus1mc5WcvSE5BX16dEOnwPay5JVS0vF2cnbEtLljsXzu5+bHVGoVKlVygqOTIxyd\nHWHvaI+iu0WSjQGw7fdasgK+ffu2VLsu06XcXHhpPM3bGk9PFOj10Ov1Npmr5GztB+PRvXNHyL2W\nlJKO9/iZI/HthjSc+e8f5sfSUnbi9q0CfLUrAV/tTEDO+YvYv+egJPmlbPm9lqyAW7dujaSkJKl2\n/0Am44PfILXaziZzlZwtilKOd6+BnVFiKMG2zbug+svjQ8YEI//qDfRrPQwDA96DW1VX9AvtYfX8\np4Ecx1uyAm7UqBH+85//IDQ0FFlZWVLF3MfLS4O8K1fM27l5eXBzdYWzs5NN5io5WxSlHO9Ofdqj\n4Qv+WLHxX4hdEQEnJ0es2PgvBPZ4HT8k74DRaMQdfSG2bd6Fps1fsHr+00CO4y1ZATs5OWHGjBmY\nPHkyEhMT0bNnT8TExGDNmjVSRaJVy+Y4euwELmRnAwCSkjcjoF0byfJE5yo5WxSlHO/3B2rxbt8P\nMKr/ZGhHxeDu3SKM6j8Zxw+eRPsurQAAdvZ2eC2gGf5z5LQkYxBNjuMt2dfQSj+vadKkCRYvXoxb\nt27hwIEDOHv2rFSRqO7ujugZEZgwJQIGgwG+Pt6IidJJlic6V8nZpUp/Oi8XpR/vZfNW4/2I4fjs\n20UoKSnBr5lHsWHVN7Jk2+J7LdkdMVJSUtC3b99yv553xFAO3hFDXrwjhryE3BHjScqXiEgJbOp7\nwEREFQkLmIhIEBYwEZEgLGAiIkFYwEREgrCAiYgEYQETEQnCAiYiEoQFTEQkCAuYiEgQFjARkSCS\nLcbzpLgYj7xELYgDKHdRHKV5K2CSsOwvdy4Qli1kMR4iIno4FjARkSAsYCIiQVjARESCsICJiARh\nARMRCcICJiIShAVMRCQIC5iISBB70QOwtj370rFo2UoUFxejgb8/Zuu0cHFxsdlc0dmldHPiUL9e\nXYQOCpYtk++17Wc3a/9/eD9qBIa0GwMA+HT7x7iae838/OY1PyA9bb9k+VLP2abOgK/n50MXHYv4\n+XHYkrQe3rVqYuHiZTabKzobAM6eO493wyfgx527ZcsE+F4rIdvLV4PQccFQQQUAqFXbC7dv3MaU\nwbPMv6QsXznmbFMFnJGZhSaNG8PXxxsAENy/L1K3brPZXNHZALAhOQV9enRDp8D2smUCfK9tPdvR\n2RFjo9/F6oXrzY81eLEejEYjZq6YggXro9B/RE+oVCrJxiDHnGUr4KKiIhQWFkqacSk3F14aT/O2\nxtMTBXo99Hq9TeaKzgYA7Qfj0b1zR8i9phPfa9vOHqkNRdrGnTj/32zzY3Z2djiceRzRYxZANyIO\nL732AroGd5AkH5BnzpIV8NmzZzF27FhMnDgRhw4dQs+ePdG9e3ekpqZKFQmT8cEloFbbSZYpMld0\ntkh8r203u/ObATAYSrD7u3T89QT3p2/2YPWH62EsMeJOQSG+W7sNzQNetnp+KTnmLFkB63Q6DBw4\nEJ06dcLIkSOxZs0afPvtt/jiiy+kioSXlwZ5V66Yt3Pz8uDm6gpnZyfJMkXmis4Wie+17Wa379Ea\n/s/7Yf66WZi+aAKcnB0xf90stOveCs/6+/zvN6qAEkOJ1fNLyTFnyQrYYDCgVatW6NSpE6pVqwaN\nRgMXFxfY20v3xYtWLZvj6LETuJB9758tScmbEdCujWR5onNFZ4vE99p2s7VD5mDiwBmYMngWYsZ+\nhLuFRZgyeBZ86tZC8Mg+UKlUcHRyQNfgDkhPy5JkDIA8c5ZsQfaJEyfCaDSipKQE2dnZaNOmDapU\nqYLjx48jPj7e4uvLuyD7voxMxC9ZDoPBAF8fb8RE6eDm6lqufVWEXGtlP+mC7DNi5sK/rl+5voZW\n3gXZ+V5XrOzyLMj+jJcHFn4VjdB2YXB0csCwKW+jYZN6UNup8e/tB7Bhecoj7ae8C7Jb43g/bEF2\nyQrYYDBg9+7dqFOnDipXrozVq1ejatWqGDJkyCN9j453xJAX74hBUuMdMf5Jss8D7O3t0aHD/35C\nOW3aNKmiiIgqJJv6HjARUUXCAiYiEoQFTEQkCAuYiEgQFjARkSAsYCIiQVjARESCsICJiARhARMR\nCcICJiIShAVMRCSIZIvxPCkuxkNS4wJEyiFyIaCNv3xe5nM8AyYiEoQFTEQkCAuYiEgQFjARkSAs\nYCIiQVjARESCsICJiARhARMRCcICJiISRLK7IouyZ186Fi1bieLiYjTw98dsnRYuLi42m8tsMdkA\noJsTh/r16iJ0ULBsmUo83iJym7X/P7wfNQJD2o0BAHy6/WNczb1mfn7zmh+Qnrb/iXNs6gz4en4+\ndNGxiJ8fhy1J6+FdqyYWLl5ms7nMFpN99tx5vBs+AT/u3C1LXiklHm8RuV6+GoSOC4YKKgBArdpe\nuH3jNqYMnmX+ZY3yBWysgDMys9CkcWP4+ngDAIL790Xq1m02m8tsMdkbklPQp0c3dApsL0teKSUe\nb7lzHZ0dMTb6XaxeuN78WIMX68FoNGLmiilYsD4K/Uf0hEqlskqeLAUs13o/l3Jz4aXxNG9rPD1R\noNdDr9fbZC6zxWRrPxiP7p07yvbnupQSj7fcuSO1oUjbuBPn/5ttfszOzg6HM48jeswC6EbE4aXX\nXkDX4A5WyZPsM+A//vgDUVFROHPmDPLy8vD888/D19cX06ZNQ40aNSTJNBkf/D+EWm0nSZ7oXGaL\nyRZFicdbztzObwbAYCjB7u/SUaOmh/nxn77ZY/7vOwWF+G7tNnQd2AGpG7Y/caZkZ8BRUVGIjIzE\nzp07sW7dOrRo0QJDhw5FRESEVJHw8tIg78oV83ZuXh7cXF3h7OwkWabIXGaLyRZFicdbztz2PVrD\n/3k/zF83C9MXTYCTsyPmr5uFdt1b4Vl/n//9RhVQYiixSqZkBXz79m34+fkBAJo2bYqDBw/ihRde\nwM2bN6WKRKuWzXH02AlcyL73z4ek5M0IaNdGsjzRucwWky2KEo+3nLnaIXMwceAMTBk8CzFjP8Ld\nwiJMGTwLPnVrIXhkH6hUKjg6OaBrcAekp2VZJVOyBdknTpyIypUro23btti1axcqV66M1157DV98\n8QU+/7zsBYpLlXdB9n0ZmYhfshwGgwG+Pt6IidLBzdW1XPuqCLnMLn/2ky7IPiNmLvzr+pXra2jl\nXZC9Ih9vkbmPuyD7M14eWPhVNELbhcHRyQHDpryNhk3qQW2nxr+3H8CG5SmPvK+HLcguWQEXFRUh\nKSkJv/32G5577jn069cPR48eRe3ateHu7m759bwjBkmMd8RQjqf1jhiS/RDO0dERgwcPvu+xpk2b\nShVHRFTh2NT3gImIKhIWMBGRICxgIiJBWMBERIKwgImIBGEBExEJwgImIhKEBUxEJAgLmIhIEBYw\nEZEgkq0FQURED8czYCIiQVjARESCsICJiARhARMRCcICJiIShAVMRCSIZHfEEMFkMmHWrFk4deoU\nHB0dERMTA19fX1nHcPjwYSxYsACJiYmy5BkMBkyfPh05OTkoLi7GqFGjEBgYKEu20WhEZGQkzp49\nC7VajaioKPj7+8uSXerq1avo168fPv/8c/NNYOUQFBSEKlWqAAB8fHwQGxsrS25CQgJ27NiB4uJi\nvPXWW+jXr58suSkpKUhOToZKpcLdu3dx8uRJpKenm4+BlAwGA6ZOnYqcnBzY29sjOjpalve6qKgI\nWq0W2dnZqFKlCmbOnIlnn33WuiEmG7Jt2zbTtGnTTCaTyXTo0CHT6NGjZc3/5JNPTD169DAFBwfL\nlrlp0yZTbGysyWQymfLz803t27eXLfvHH380TZ8+3WQymUz79++X/XgXFxebxowZY+rcubPpzJkz\nsuXevXvX1LdvX9nySu3fv980atQok8lkMhUUFJgWL14s+xhMJpMpKirK9PXXX8uWt337dtP48eNN\nJpPJlJ6ebgoPD5cld+3atSadTmcymUymM2fOmIYNG2b1DJv6COKXX35Bmzb3bln90ksv4dixY7Lm\n165dG0uXLpU1s2vXrhg3bhyAe2ek9vby/aPmjTfeQHR0NAAgJycHVatWlS0bAObNm4dBgwbB09NT\n1tyTJ09Cr9dj+PDheOedd3D48GFZcvft24cGDRogLCwMo0ePRkBAgCy5f3X06FH89ttvePPNN2XL\nrFOnDkpKSmAymXDr1i04yHRD099++w1t27YFAPj5+eHMmTNWz7CpjyBu374N17/crtre3h5GoxFq\ntTx/z3Ts2BE5OTmyZJWqVKkSgHtzHzduHCZMmCBrvlqtxrRp07B9+3Z8/PHHsuUmJyfDw8MDrVu3\nxooVK2TLBQBnZ2cMHz4cb775Js6dO4d3330XaWlpkv85u379Ov7880+sXLkSFy5cwOjRo7F161ZJ\nM/8uISEB77//vqyZlStXRnZ2Nrp06YL8/HysXLlSltznnnsOu3btwhtvvIFDhw4hLy8PJpMJKpXK\nahk2dQZcpUoVFBQUmLflLF+RLl68iCFDhqBv377o1q2b7Plz585FWloaIiMjUVhYKEtmcnIy0tPT\nERISgpMnT2Lq1Km4evWqLNl16tRBr169zP9drVo1XL58WfLcatWqoU2bNrC3t4efnx+cnJxw7do1\nyXNL3bp1C+fOnUPz5s1lywSA1atXo02bNkhLS8OWLVswdepUFBUVSZ7br18/VK5cGYMHD8ZPP/2E\n559/3qrlC9hYAb/88svYvXs3AODQoUNo0KCBkHGYZFxe48qVKxg+fDgmT56Mvn37ypYLAJs3b0ZC\nQgIAwMnJCWq1Wra/8NauXYvExEQkJiaiUaNGmDdvHjw8PGTJ3rRpE+bOnQsAyM3NRUFBAWrUqCF5\n7iuvvIK9e/eacwsLC+Hu7i55bqkDBw6gZcuWsuWVqlq1qvmHfa6urjAYDDAajZLnHj16FK+99hrW\nrVuHzp07S/IDfZv6CKJjx45IT0/HwIEDAQBxcXFCxmHtvyUfZuXKlbh58yaWLVuGpUuXQqVSYdWq\nVXB0dJQ8u1OnTtBqtXj77bdhMBgQEREhS+7fyXm8AaB///7QarV46623oFarERsbK8tfPO3bt8fP\nP/+M/v37w2QyYebMmbLO/ezZs7J/qwgAhgwZgunTp2Pw4MEwGAyYOHEinJ2dJc+tXbs2Fi1ahBUr\nVsDNzQ0xMTFWz+BqaEREgtjURxBERBUJC5iISBAWMBGRICxgIiJBWMBERIKwgImIBGEB01Pv9u3b\nGDNmjNX3m5OTY3HluCVLlmDJkiVW3SdRKRYwPfXy8/Nx8uRJSfYtxYUMcl8YQhUXC5ieejExMcjL\ny0N4eDhycnLQpUsXDB48GMOGDUNKSgq0Wq3594aEhODAgQMA7i0cExQUhD59+mDBggUPzTh9+jRC\nQ0Px5ptvIjAwEGvXrjU/d+TIEQwYMAA9e/bEmjVrzI8/zv6JHoQFTE+9yMhIeHp6YvHixQCA8+fP\nY8GCBfjss8/KfM3evXtx/PhxbNq0CSkpKbh06RK+/fbbMn//xo0bERYWhqSkJHzxxRdYuHCh+bkr\nV64gMTER69evx7p163Dy5MnH3j/Rg9jUWhCkDB4eHqhZs+ZDf09GRgaOHj2KoKAgmEwm3L17F97e\n3mX+/mnTpmHv3r1ISEjAqVOncOfOHfNz3bp1g5OTE5ycnBAYGIisrCxcvHjxgft/+eWXrTZPsn0s\nYKpwnJyczP/9989bDQYDgHtLkYaGhuKdd94BcO8HeXZ2dmXuc9y4cahWrRoCAgLQrVs3pKammp/7\n6yL3RqMRDg4OMJlMD9y/nMtDUsXHjyDoqWdvb4+SkhLz9l/Xj3J3d8fvv/8OALhw4QJOnToFAGjZ\nsiW2bNkCvV4Pg8GA0aNHIy0trcyMjIwMjB071nyG+9ecrVu3oqioCDdu3MCuXbvQokULtGjRosz9\nc30relQ8A6annoeHB7y8vDBkyBDExsbed9b72muvYdOmTejSpQvq1q2LV199FQAQEBCAU6dOYcCA\nATAajWjbti369OlTZkZ4eDgGDRoENzc3+Pn5wcfHB9nZ2QAAb29vDBo0CEVFRRg1ahTq1q2LunXr\nPnD/OTk5/BYEPTIuR0lEJAg/giAiEoQFTEQkCAuYiEgQFjARkSAsYCIiQVjARESCsICJiARhARMR\nCfL/ADUiXKng20C6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c1ccf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(ytest, ypred)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Random Forests\n",
    "\n",
    "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
    "Random forests are a powerful method with several advantages:\n",
    "\n",
    "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
    "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
    "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
    "\n",
    "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
